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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:1000000 |
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- loss:MSELoss |
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base_model: distilbert/distilbert-base-multilingual-cased |
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widget: |
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- source_sentence: "Agreement on Pledge and Right of Disposal of Cash Account based\ |
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\ on agreement template dated 4 June 2015 1. This agreement (the â\x80\x9CPledged\ |
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\ Cash Account Agreementâ\x80\x9D or â\x80\x9CAgreementâ\x80\x9D) has been entered\ |
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\ into on this. [insert the day] day of [month] [year] by and between." |
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sentences: |
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- Perjanjian Gadai dan Hak Pelepasan Rekening Kas berdasarkan template perjanjian |
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tanggal 4 Juni 2015 1. Perjanjian ini (¢Â€ÂœPerjanjian Gadai Rekening Kas†|
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atau ¢ €ÂœPerjanjian†) telah ditandatangani mengenai hal ini. |
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[masukkan hari] hari [bulan] [tahun] oleh dan di antara. |
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- verizon.net yahoo |
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- Teori sosial-kognitif adalah perspektif teoretis di mana belajar dengan mengamati |
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orang lain adalah fokus studi. Teori sosial-kognitif didasarkan pada beberapa |
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asumsi dasar. Salah satunya adalah bahwa orang dapat belajar dengan mengamati |
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orang lain. Pembelajar dapat memperoleh perilaku dan pengetahuan baru hanya dengan |
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mengamati model. Teori sosial-kognitif berfokus pada pembelajaran yang terjadi |
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dalam konteks sosial. Dalam konteks sosial ini, peserta didik mengamati perilaku |
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dan keterampilan baru dari seorang model. Mereka mungkin menginternalisasi dan |
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mendemonstrasikan keterampilan tersebut atau terkadang tidak. |
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- source_sentence: a poem of six six-line stanzas and a three-line envoy, originally |
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without rhyme, in which each stanza repeats the end words of the lines of the |
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first stanza, but in different order, the envoy using the six words again, three |
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in the middle of the lines and three at the end. 1 Also called sextain. |
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sentences: |
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- puisi enam bait enam baris dan utusan tiga baris, awalnya tanpa sajak, di mana |
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setiap bait mengulangi kata-kata akhir dari baris bait pertama, tetapi dalam urutan |
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yang berbeda, utusan menggunakan enam kata lagi, tiga di tengah baris dan tiga |
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di akhir. 1 Disebut juga sextain. |
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- Lipase lambung adalah enzim pencernaan lain yang dibuat oleh sel utama. Ini membantu |
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memecah lemak rantai pendek dan menengah. Amilase juga ditemukan dalam cairan |
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lambung, tetapi tidak dibuat oleh lambung. Enzim ini berasal dari air liur dan |
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berjalan bersama dengan bolus ke dalam lambung. |
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- Masuk dengan ID Apple Anda dan atur iCloud Drive. Masukkan ID Apple dan kata sandi |
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Anda. Untuk melewati langkah ini, atau jika Anda tidak memiliki ID Apple, ketuk |
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Tidak memiliki ID Apple atau lupa. Jika Anda menggunakan lebih dari satu ID, ketuk |
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Gunakan ID Apple yang berbeda untuk iCloud dan iTunes.masuk dengan ID Apple Anda |
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dan atur iCloud Drive. Masukkan ID Apple dan kata sandi Anda. Untuk melewati langkah |
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ini, atau jika Anda tidak memiliki ID Apple, ketuk Tidak memiliki ID Apple atau |
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lupa. Jika Anda menggunakan lebih dari satu ID, ketuk Gunakan ID Apple yang berbeda |
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untuk iCloud dan iTunes. |
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- source_sentence: "Ways to Calculate GSAâ\x80\x99s. Industrial Funding Fee (IFF)\ |
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\ Divide the original unit price by one (1) minus the IFF rate (e.g., 1 â\x80\x93\ |
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\ 0.0075 = 0.9925) to establish the contract price. For example, if the original\ |
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\ price of $198.50 is divided by .9925 , the selling price will be $200." |
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sentences: |
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- Kompetisi Pelatih Pertunjukan Dr. Oz. Dr. Oz sedang mencari pelatih untuk bergabung |
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dalam acara ini untuk membantu menginspirasi jutaan orang lain di rumah agar bugar |
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dan sehat! LIHAT SELENGKAPNYA |
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- perbedaan antara tulang rusuk sejati, tulang rusuk palsu, dan tulang rusuk melayang |
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- Cara Menghitung GSA¢Âââ. Biaya Pendanaan Industri (IFF) Bagi harga satuan asli |
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dengan satu (1) dikurangi tarif IFF (misalnya, 1 ¢Â€Â“ 0,0075 = 0,9925) |
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untuk menetapkan harga kontrak. Misalnya, jika harga asli $198,50 dibagi ,9925 |
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, harga jualnya adalah $200. |
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- source_sentence: how does one get a scrapped vehicle registered again |
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sentences: |
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- bagaimana cara mendapatkan kendaraan bekas yang didaftarkan kembali? |
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- definisi bisnis yang bertanggung jawab |
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- apakah cwu mendapatkan perguruan tinggi? |
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- source_sentence: "1 Concrete is typically measured by cubic yards (3â\x80\x99x3â\x80\ |
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\x99x3â\x80\x99). 2 An average cost for a cubic yard of concrete is $75 to $125,\ |
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\ depending on how much is needed and local prices. 3 Labor costs to pour and\ |
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\ form concrete run somewhere around $3.50 to $7.00 per square foot. An average\ |
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\ cost for a cubic yard of concrete is $75 to $125, depending on how much is needed\ |
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\ and local prices. 2 Labor costs to pour and form concrete run somewhere around\ |
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\ $3.50 to $7.00 per square foot." |
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sentences: |
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- 1 Beton biasanya diukur dengan meter kubik (3âÂâââx3Ãâ¢Ã‚ââx3âÂââ„¢). |
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2 Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung |
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pada berapa banyak yang dibutuhkan dan harga setempat. 3 Biaya tenaga kerja untuk |
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menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi. |
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Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung |
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pada berapa banyak yang dibutuhkan dan harga lokal. 2 Biaya tenaga kerja untuk |
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menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi. |
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- seberapa terlambat alat utara terbuka? |
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- Parrot Tattoos - Polly ingin cracker.. Ungkapan ini identik dengan 'parrot', terutama |
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yang duduk di bahu bajak laut, seperti yang dibuat terkenal dalam cerita klasik |
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Robert Louis Stevenson, Treasure Island (1883).lint', yang mungkin tidak adalah |
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burung beo pertama yang diasosiasikan dengan nama 'Polly', tapi dia pasti mempopulerkannya. |
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Dan Polly tentu memastikan burung beo itu akan menjadi simbol ikonik dari tradisi |
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bajak laut. Sebagai pendamping legendaris bagi manusia, burung beo menyarankan |
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semacam wali. |
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datasets: |
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- carles-undergrad-thesis/en-id-parallel-sentences |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- negative_mse |
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- src2trg_accuracy |
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- trg2src_accuracy |
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- mean_accuracy |
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model-index: |
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- name: SentenceTransformer based on distilbert/distilbert-base-multilingual-cased |
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results: |
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- task: |
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type: knowledge-distillation |
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name: Knowledge Distillation |
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dataset: |
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name: default |
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type: default |
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metrics: |
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- type: negative_mse |
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value: -3.55539433658123 |
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name: Negative Mse |
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- task: |
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type: translation |
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name: Translation |
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dataset: |
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name: default |
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type: default |
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metrics: |
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- type: src2trg_accuracy |
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value: 0.9894 |
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name: Src2Trg Accuracy |
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- type: trg2src_accuracy |
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value: 0.9861 |
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name: Trg2Src Accuracy |
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- type: mean_accuracy |
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value: 0.9877499999999999 |
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name: Mean Accuracy |
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--- |
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|
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# SentenceTransformer based on distilbert/distilbert-base-multilingual-cased |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co./distilbert/distilbert-base-multilingual-cased) on the [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co./distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'1 Concrete is typically measured by cubic yards (3â\x80\x99x3â\x80\x99x3â\x80\x99). 2 An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 3 Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot. An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 2 Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot.', |
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'1 Beton biasanya diukur dengan meter kubik (3âÂâââx3Ãâ¢Ã‚ââx3âÂââ„¢). 2 Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang dibutuhkan dan harga setempat. 3 Biaya tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi. Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang dibutuhkan dan harga lokal. 2 Biaya tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi.', |
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"Parrot Tattoos - Polly ingin cracker.. Ungkapan ini identik dengan 'parrot', terutama yang duduk di bahu bajak laut, seperti yang dibuat terkenal dalam cerita klasik Robert Louis Stevenson, Treasure Island (1883).lint', yang mungkin tidak adalah burung beo pertama yang diasosiasikan dengan nama 'Polly', tapi dia pasti mempopulerkannya. Dan Polly tentu memastikan burung beo itu akan menjadi simbol ikonik dari tradisi bajak laut. Sebagai pendamping legendaris bagi manusia, burung beo menyarankan semacam wali.", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Knowledge Distillation |
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* Dataset: `default` |
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* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) |
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| Metric | Value | |
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|:-----------------|:------------| |
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| **negative_mse** | **-3.5554** | |
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#### Translation |
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* Dataset: `default` |
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* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) |
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| Metric | Value | |
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|:------------------|:-----------| |
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| src2trg_accuracy | 0.9894 | |
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| trg2src_accuracy | 0.9861 | |
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| **mean_accuracy** | **0.9877** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### default |
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* Dataset: [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) at [c8bc0cb](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences/tree/c8bc0cbe69684da379af3d36424041162d63e702) |
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* Size: 1,000,000 training samples |
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* Columns: <code>english</code>, <code>indonesian</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | english | indonesian | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| |
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| type | string | string | list | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 44.27 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 48.93 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> | |
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* Samples: |
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| english | indonesian | label | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| |
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| <code>This sample job description shares how one smaller sized, growing, multi-site nonprofit organization configured the role of executive director.The executive director is responsible for general management as well as designing a national expansion plan. There also is a heavy emphasis on program evaluation.Feel free to use this sample job description in creating one for your organization.osition. Reporting to the Board of Directors, the Executive Director (ED) will have overall strategic and operational responsibility for XYZ Nonprofit's staff, programs, expansion, and execution of its mission. S/he will initially develop deep knowledge of field, core programs, operations, and business plans.</code> | <code>Uraian tugas contoh ini membagikan bagaimana satu organisasi nirlaba multi-situs berukuran lebih kecil, berkembang, mengonfigurasi peran direktur eksekutif. Direktur eksekutif bertanggung jawab atas manajemen umum serta merancang rencana ekspansi nasional. Ada juga penekanan berat pada evaluasi program. Jangan ragu untuk menggunakan contoh deskripsi pekerjaan ini dalam membuat satu untuk posisi organisasi Anda. Melaporkan kepada Dewan Direksi, Direktur Eksekutif (ED) akan memiliki tanggung jawab strategis dan operasional secara keseluruhan untuk staf, program, ekspansi, dan pelaksanaan misi XYZ Nirlaba. Dia awalnya akan mengembangkan pengetahuan yang mendalam tentang lapangan, program inti, operasi, dan rencana bisnis.</code> | <code>[-0.4337165653705597, -0.0650932714343071, -0.04308838024735451, -0.1756953001022339, 0.32854965329170227, ...]</code> | |
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| <code>Industrial revolution occured last in Russia. In Germany, France and United States industrial revolution occured in early-to-mid 1800's. While in Russia creation of railroads, and foundation of factories happened by govermental initiatives towards the end of XIX century.n Germany, France and United States industrial revolution occured in early-to-mid 1800's.</code> | <code>Revolusi industri terakhir terjadi di Rusia. Di Jerman, Perancis dan Amerika Serikat terjadi revolusi industri pada awal hingga pertengahan 1800-an. Sedangkan di Rusia pembuatan rel kereta api, dan pendirian pabrik terjadi atas inisiatif pemerintah menjelang akhir abad XIX. Revolusi industri Jerman, Prancis dan Amerika Serikat terjadi pada awal hingga pertengahan 1800-an.</code> | <code>[-0.22887374460697174, -0.17583712935447693, 0.08270637691020966, -0.15496928989887238, -0.18010610342025757, ...]</code> | |
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| <code>what causes hordeolum internum left lower eyelid</code> | <code>apa penyebab hordeolum internum kelopak mata kiri bawah</code> | <code>[-0.19872592389583588, 0.4119395911693573, 0.3756648004055023, -0.4884617030620575, 0.15375499427318573, ...]</code> | |
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* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) |
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|
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### Evaluation Dataset |
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#### default |
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* Dataset: [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) at [c8bc0cb](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences/tree/c8bc0cbe69684da379af3d36424041162d63e702) |
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* Size: 1,000,000 evaluation samples |
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* Columns: <code>english</code>, <code>indonesian</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | english | indonesian | label | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------| |
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| type | string | string | list | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 46.58 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 51.0 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> | |
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* Samples: |
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| english | indonesian | label | |
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|:---------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| |
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| <code>do appraisers give adjustments for lot size</code> | <code>apakah penilai memberikan penyesuaian untuk ukuran lot?</code> | <code>[0.12256570905447006, 0.011573846451938152, -0.19426874816417694, -0.17596185207366943, 0.35024771094322205, ...]</code> | |
|
| <code>hotels in binghamton ny</code> | <code>hotel di binghamton ny</code> | <code>[0.14259624481201172, -0.048470016568899155, 0.1078888401389122, 0.06728225946426392, 0.6096671223640442, ...]</code> | |
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| <code>guitarist kenny greenberg</code> | <code>gitaris kenny greenberg</code> | <code>[-0.6973275542259216, 0.27737292647361755, -0.09295299649238586, 0.24035970866680145, 0.154855415225029, ...]</code> | |
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* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: False |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | default loss | default_negative_mse | default_mean_accuracy | |
|
|:------:|:-----:|:-------------:|:------------:|:--------------------:|:---------------------:| |
|
| 0.0065 | 100 | 0.1968 | - | - | - | |
|
| 0.0129 | 200 | 0.1797 | - | - | - | |
|
| 0.0194 | 300 | 0.1596 | - | - | - | |
|
| 0.0259 | 400 | 0.1367 | - | - | - | |
|
| 0.0323 | 500 | 0.1167 | - | - | - | |
|
| 0.0388 | 600 | 0.103 | - | - | - | |
|
| 0.0453 | 700 | 0.0954 | - | - | - | |
|
| 0.0517 | 800 | 0.0909 | - | - | - | |
|
| 0.0582 | 900 | 0.088 | - | - | - | |
|
| 0.0646 | 1000 | 0.0861 | - | - | - | |
|
| 0.0711 | 1100 | 0.0847 | - | - | - | |
|
| 0.0776 | 1200 | 0.082 | - | - | - | |
|
| 0.0840 | 1300 | 0.0818 | - | - | - | |
|
| 0.0905 | 1400 | 0.0813 | - | - | - | |
|
| 0.0970 | 1500 | 0.0804 | - | - | - | |
|
| 0.1034 | 1600 | 0.0817 | - | - | - | |
|
| 0.1099 | 1700 | 0.0799 | - | - | - | |
|
| 0.1164 | 1800 | 0.0804 | - | - | - | |
|
| 0.1228 | 1900 | 0.0802 | - | - | - | |
|
| 0.1293 | 2000 | 0.0791 | - | - | - | |
|
| 0.1358 | 2100 | 0.0789 | - | - | - | |
|
| 0.1422 | 2200 | 0.0783 | - | - | - | |
|
| 0.1487 | 2300 | 0.0783 | - | - | - | |
|
| 0.1551 | 2400 | 0.077 | - | - | - | |
|
| 0.1616 | 2500 | 0.0762 | - | - | - | |
|
| 0.1681 | 2600 | 0.0762 | - | - | - | |
|
| 0.1745 | 2700 | 0.0754 | - | - | - | |
|
| 0.1810 | 2800 | 0.075 | - | - | - | |
|
| 0.1875 | 2900 | 0.0735 | - | - | - | |
|
| 0.1939 | 3000 | 0.0745 | - | - | - | |
|
| 0.2004 | 3100 | 0.0739 | - | - | - | |
|
| 0.2069 | 3200 | 0.0732 | - | - | - | |
|
| 0.2133 | 3300 | 0.0724 | - | - | - | |
|
| 0.2198 | 3400 | 0.0727 | - | - | - | |
|
| 0.2263 | 3500 | 0.0726 | - | - | - | |
|
| 0.2327 | 3600 | 0.071 | - | - | - | |
|
| 0.2392 | 3700 | 0.0713 | - | - | - | |
|
| 0.2457 | 3800 | 0.0708 | - | - | - | |
|
| 0.2521 | 3900 | 0.0704 | - | - | - | |
|
| 0.2586 | 4000 | 0.0703 | - | - | - | |
|
| 0.2650 | 4100 | 0.0704 | - | - | - | |
|
| 0.2715 | 4200 | 0.0695 | - | - | - | |
|
| 0.2780 | 4300 | 0.068 | - | - | - | |
|
| 0.2844 | 4400 | 0.0681 | - | - | - | |
|
| 0.2909 | 4500 | 0.0683 | - | - | - | |
|
| 0.2974 | 4600 | 0.0674 | - | - | - | |
|
| 0.3038 | 4700 | 0.0683 | - | - | - | |
|
| 0.3103 | 4800 | 0.0674 | - | - | - | |
|
| 0.3168 | 4900 | 0.0674 | - | - | - | |
|
| 0.3232 | 5000 | 0.0666 | - | - | - | |
|
| 0.3297 | 5100 | 0.0677 | - | - | - | |
|
| 0.3362 | 5200 | 0.066 | - | - | - | |
|
| 0.3426 | 5300 | 0.0655 | - | - | - | |
|
| 0.3491 | 5400 | 0.0658 | - | - | - | |
|
| 0.3555 | 5500 | 0.0658 | - | - | - | |
|
| 0.3620 | 5600 | 0.0646 | - | - | - | |
|
| 0.3685 | 5700 | 0.0638 | - | - | - | |
|
| 0.3749 | 5800 | 0.065 | - | - | - | |
|
| 0.3814 | 5900 | 0.0648 | - | - | - | |
|
| 0.3879 | 6000 | 0.0636 | - | - | - | |
|
| 0.3943 | 6100 | 0.0637 | - | - | - | |
|
| 0.4008 | 6200 | 0.0636 | - | - | - | |
|
| 0.4073 | 6300 | 0.0633 | - | - | - | |
|
| 0.4137 | 6400 | 0.0629 | - | - | - | |
|
| 0.4202 | 6500 | 0.0638 | - | - | - | |
|
| 0.4267 | 6600 | 0.0625 | - | - | - | |
|
| 0.4331 | 6700 | 0.0615 | - | - | - | |
|
| 0.4396 | 6800 | 0.062 | - | - | - | |
|
| 0.4461 | 6900 | 0.062 | - | - | - | |
|
| 0.4525 | 7000 | 0.0614 | - | - | - | |
|
| 0.4590 | 7100 | 0.0622 | - | - | - | |
|
| 0.4654 | 7200 | 0.061 | - | - | - | |
|
| 0.4719 | 7300 | 0.06 | - | - | - | |
|
| 0.4784 | 7400 | 0.0606 | - | - | - | |
|
| 0.4848 | 7500 | 0.0606 | - | - | - | |
|
| 0.4913 | 7600 | 0.0597 | - | - | - | |
|
| 0.4978 | 7700 | 0.0598 | - | - | - | |
|
| 0.5042 | 7800 | 0.0594 | - | - | - | |
|
| 0.5107 | 7900 | 0.0596 | - | - | - | |
|
| 0.5172 | 8000 | 0.0584 | - | - | - | |
|
| 0.5236 | 8100 | 0.0589 | - | - | - | |
|
| 0.5301 | 8200 | 0.0587 | - | - | - | |
|
| 0.5366 | 8300 | 0.059 | - | - | - | |
|
| 0.5430 | 8400 | 0.0592 | - | - | - | |
|
| 0.5495 | 8500 | 0.058 | - | - | - | |
|
| 0.5560 | 8600 | 0.0576 | - | - | - | |
|
| 0.5624 | 8700 | 0.0577 | - | - | - | |
|
| 0.5689 | 8800 | 0.0575 | - | - | - | |
|
| 0.5753 | 8900 | 0.0576 | - | - | - | |
|
| 0.5818 | 9000 | 0.0575 | - | - | - | |
|
| 0.5883 | 9100 | 0.0567 | - | - | - | |
|
| 0.5947 | 9200 | 0.0568 | - | - | - | |
|
| 0.6012 | 9300 | 0.0558 | - | - | - | |
|
| 0.6077 | 9400 | 0.0558 | - | - | - | |
|
| 0.6141 | 9500 | 0.0563 | - | - | - | |
|
| 0.6206 | 9600 | 0.0565 | - | - | - | |
|
| 0.6271 | 9700 | 0.0547 | - | - | - | |
|
| 0.6335 | 9800 | 0.0555 | - | - | - | |
|
| 0.6400 | 9900 | 0.0551 | - | - | - | |
|
| 0.6465 | 10000 | 0.055 | - | - | - | |
|
| 0.6529 | 10100 | 0.0553 | - | - | - | |
|
| 0.6594 | 10200 | 0.0548 | - | - | - | |
|
| 0.6658 | 10300 | 0.0542 | - | - | - | |
|
| 0.6723 | 10400 | 0.0551 | - | - | - | |
|
| 0.6788 | 10500 | 0.0545 | - | - | - | |
|
| 0.6852 | 10600 | 0.0545 | - | - | - | |
|
| 0.6917 | 10700 | 0.0542 | - | - | - | |
|
| 0.6982 | 10800 | 0.0538 | - | - | - | |
|
| 0.7046 | 10900 | 0.0532 | - | - | - | |
|
| 0.7111 | 11000 | 0.0534 | - | - | - | |
|
| 0.7176 | 11100 | 0.053 | - | - | - | |
|
| 0.7240 | 11200 | 0.0534 | - | - | - | |
|
| 0.7305 | 11300 | 0.0532 | - | - | - | |
|
| 0.7370 | 11400 | 0.0535 | - | - | - | |
|
| 0.7434 | 11500 | 0.0533 | - | - | - | |
|
| 0.7499 | 11600 | 0.0532 | - | - | - | |
|
| 0.7564 | 11700 | 0.053 | - | - | - | |
|
| 0.7628 | 11800 | 0.0526 | - | - | - | |
|
| 0.7693 | 11900 | 0.0527 | - | - | - | |
|
| 0.7757 | 12000 | 0.053 | - | - | - | |
|
| 0.7822 | 12100 | 0.0522 | - | - | - | |
|
| 0.7887 | 12200 | 0.0521 | - | - | - | |
|
| 0.7951 | 12300 | 0.0524 | - | - | - | |
|
| 0.8016 | 12400 | 0.0518 | - | - | - | |
|
| 0.8081 | 12500 | 0.0521 | - | - | - | |
|
| 0.8145 | 12600 | 0.0516 | - | - | - | |
|
| 0.8210 | 12700 | 0.0517 | - | - | - | |
|
| 0.8275 | 12800 | 0.0511 | - | - | - | |
|
| 0.8339 | 12900 | 0.0517 | - | - | - | |
|
| 0.8404 | 13000 | 0.0516 | - | - | - | |
|
| 0.8469 | 13100 | 0.0516 | - | - | - | |
|
| 0.8533 | 13200 | 0.0509 | - | - | - | |
|
| 0.8598 | 13300 | 0.0508 | - | - | - | |
|
| 0.8662 | 13400 | 0.0506 | - | - | - | |
|
| 0.8727 | 13500 | 0.0507 | - | - | - | |
|
| 0.8792 | 13600 | 0.0507 | - | - | - | |
|
| 0.8856 | 13700 | 0.0503 | - | - | - | |
|
| 0.8921 | 13800 | 0.0504 | - | - | - | |
|
| 0.8986 | 13900 | 0.0506 | - | - | - | |
|
| 0.9050 | 14000 | 0.0507 | - | - | - | |
|
| 0.9115 | 14100 | 0.0503 | - | - | - | |
|
| 0.9180 | 14200 | 0.0496 | - | - | - | |
|
| 0.9244 | 14300 | 0.0498 | - | - | - | |
|
| 0.9309 | 14400 | 0.0499 | - | - | - | |
|
| 0.9374 | 14500 | 0.0504 | - | - | - | |
|
| 0.9438 | 14600 | 0.0493 | - | - | - | |
|
| 0.9503 | 14700 | 0.0495 | - | - | - | |
|
| 0.9568 | 14800 | 0.0493 | - | - | - | |
|
| 0.9632 | 14900 | 0.0494 | - | - | - | |
|
| 0.9697 | 15000 | 0.0495 | - | - | - | |
|
| 0.9761 | 15100 | 0.0496 | - | - | - | |
|
| 0.9826 | 15200 | 0.0486 | - | - | - | |
|
| 0.9891 | 15300 | 0.0491 | - | - | - | |
|
| 0.9955 | 15400 | 0.0485 | - | - | - | |
|
| 1.0 | 15469 | - | 0.0479 | -4.9594 | 0.9693 | |
|
| 1.0020 | 15500 | 0.0487 | - | - | - | |
|
| 1.0085 | 15600 | 0.0488 | - | - | - | |
|
| 1.0149 | 15700 | 0.0482 | - | - | - | |
|
| 1.0214 | 15800 | 0.0486 | - | - | - | |
|
| 1.0279 | 15900 | 0.0487 | - | - | - | |
|
| 1.0343 | 16000 | 0.0487 | - | - | - | |
|
| 1.0408 | 16100 | 0.0484 | - | - | - | |
|
| 1.0473 | 16200 | 0.0478 | - | - | - | |
|
| 1.0537 | 16300 | 0.0478 | - | - | - | |
|
| 1.0602 | 16400 | 0.048 | - | - | - | |
|
| 1.0666 | 16500 | 0.048 | - | - | - | |
|
| 1.0731 | 16600 | 0.048 | - | - | - | |
|
| 1.0796 | 16700 | 0.0478 | - | - | - | |
|
| 1.0860 | 16800 | 0.0478 | - | - | - | |
|
| 1.0925 | 16900 | 0.0478 | - | - | - | |
|
| 1.0990 | 17000 | 0.0472 | - | - | - | |
|
| 1.1054 | 17100 | 0.048 | - | - | - | |
|
| 1.1119 | 17200 | 0.047 | - | - | - | |
|
| 1.1184 | 17300 | 0.0477 | - | - | - | |
|
| 1.1248 | 17400 | 0.0476 | - | - | - | |
|
| 1.1313 | 17500 | 0.0473 | - | - | - | |
|
| 1.1378 | 17600 | 0.0474 | - | - | - | |
|
| 1.1442 | 17700 | 0.0472 | - | - | - | |
|
| 1.1507 | 17800 | 0.0473 | - | - | - | |
|
| 1.1572 | 17900 | 0.0468 | - | - | - | |
|
| 1.1636 | 18000 | 0.047 | - | - | - | |
|
| 1.1701 | 18100 | 0.0471 | - | - | - | |
|
| 1.1765 | 18200 | 0.0467 | - | - | - | |
|
| 1.1830 | 18300 | 0.0464 | - | - | - | |
|
| 1.1895 | 18400 | 0.0463 | - | - | - | |
|
| 1.1959 | 18500 | 0.047 | - | - | - | |
|
| 1.2024 | 18600 | 0.0463 | - | - | - | |
|
| 1.2089 | 18700 | 0.0466 | - | - | - | |
|
| 1.2153 | 18800 | 0.0458 | - | - | - | |
|
| 1.2218 | 18900 | 0.0465 | - | - | - | |
|
| 1.2283 | 19000 | 0.0466 | - | - | - | |
|
| 1.2347 | 19100 | 0.0459 | - | - | - | |
|
| 1.2412 | 19200 | 0.0464 | - | - | - | |
|
| 1.2477 | 19300 | 0.0457 | - | - | - | |
|
| 1.2541 | 19400 | 0.0459 | - | - | - | |
|
| 1.2606 | 19500 | 0.0463 | - | - | - | |
|
| 1.2671 | 19600 | 0.0458 | - | - | - | |
|
| 1.2735 | 19700 | 0.0463 | - | - | - | |
|
| 1.2800 | 19800 | 0.0449 | - | - | - | |
|
| 1.2864 | 19900 | 0.0455 | - | - | - | |
|
| 1.2929 | 20000 | 0.0457 | - | - | - | |
|
| 1.2994 | 20100 | 0.0455 | - | - | - | |
|
| 1.3058 | 20200 | 0.0456 | - | - | - | |
|
| 1.3123 | 20300 | 0.0453 | - | - | - | |
|
| 1.3188 | 20400 | 0.0453 | - | - | - | |
|
| 1.3252 | 20500 | 0.0454 | - | - | - | |
|
| 1.3317 | 20600 | 0.0458 | - | - | - | |
|
| 1.3382 | 20700 | 0.0449 | - | - | - | |
|
| 1.3446 | 20800 | 0.0449 | - | - | - | |
|
| 1.3511 | 20900 | 0.0454 | - | - | - | |
|
| 1.3576 | 21000 | 0.0448 | - | - | - | |
|
| 1.3640 | 21100 | 0.0445 | - | - | - | |
|
| 1.3705 | 21200 | 0.0445 | - | - | - | |
|
| 1.3769 | 21300 | 0.045 | - | - | - | |
|
| 1.3834 | 21400 | 0.0448 | - | - | - | |
|
| 1.3899 | 21500 | 0.0444 | - | - | - | |
|
| 1.3963 | 21600 | 0.0446 | - | - | - | |
|
| 1.4028 | 21700 | 0.0446 | - | - | - | |
|
| 1.4093 | 21800 | 0.0444 | - | - | - | |
|
| 1.4157 | 21900 | 0.0449 | - | - | - | |
|
| 1.4222 | 22000 | 0.0447 | - | - | - | |
|
| 1.4287 | 22100 | 0.044 | - | - | - | |
|
| 1.4351 | 22200 | 0.0444 | - | - | - | |
|
| 1.4416 | 22300 | 0.044 | - | - | - | |
|
| 1.4481 | 22400 | 0.0443 | - | - | - | |
|
| 1.4545 | 22500 | 0.0443 | - | - | - | |
|
| 1.4610 | 22600 | 0.0445 | - | - | - | |
|
| 1.4675 | 22700 | 0.0436 | - | - | - | |
|
| 1.4739 | 22800 | 0.0438 | - | - | - | |
|
| 1.4804 | 22900 | 0.0441 | - | - | - | |
|
| 1.4868 | 23000 | 0.0437 | - | - | - | |
|
| 1.4933 | 23100 | 0.0434 | - | - | - | |
|
| 1.4998 | 23200 | 0.0437 | - | - | - | |
|
| 1.5062 | 23300 | 0.0435 | - | - | - | |
|
| 1.5127 | 23400 | 0.0437 | - | - | - | |
|
| 1.5192 | 23500 | 0.043 | - | - | - | |
|
| 1.5256 | 23600 | 0.0434 | - | - | - | |
|
| 1.5321 | 23700 | 0.0436 | - | - | - | |
|
| 1.5386 | 23800 | 0.0439 | - | - | - | |
|
| 1.5450 | 23900 | 0.0438 | - | - | - | |
|
| 1.5515 | 24000 | 0.0433 | - | - | - | |
|
| 1.5580 | 24100 | 0.0429 | - | - | - | |
|
| 1.5644 | 24200 | 0.0433 | - | - | - | |
|
| 1.5709 | 24300 | 0.0428 | - | - | - | |
|
| 1.5773 | 24400 | 0.0434 | - | - | - | |
|
| 1.5838 | 24500 | 0.0432 | - | - | - | |
|
| 1.5903 | 24600 | 0.0433 | - | - | - | |
|
| 1.5967 | 24700 | 0.0426 | - | - | - | |
|
| 1.6032 | 24800 | 0.0426 | - | - | - | |
|
| 1.6097 | 24900 | 0.0425 | - | - | - | |
|
| 1.6161 | 25000 | 0.0432 | - | - | - | |
|
| 1.6226 | 25100 | 0.043 | - | - | - | |
|
| 1.6291 | 25200 | 0.042 | - | - | - | |
|
| 1.6355 | 25300 | 0.0427 | - | - | - | |
|
| 1.6420 | 25400 | 0.0425 | - | - | - | |
|
| 1.6485 | 25500 | 0.0422 | - | - | - | |
|
| 1.6549 | 25600 | 0.0428 | - | - | - | |
|
| 1.6614 | 25700 | 0.0423 | - | - | - | |
|
| 1.6679 | 25800 | 0.0422 | - | - | - | |
|
| 1.6743 | 25900 | 0.0425 | - | - | - | |
|
| 1.6808 | 26000 | 0.0424 | - | - | - | |
|
| 1.6872 | 26100 | 0.0426 | - | - | - | |
|
| 1.6937 | 26200 | 0.0422 | - | - | - | |
|
| 1.7002 | 26300 | 0.0419 | - | - | - | |
|
| 1.7066 | 26400 | 0.0416 | - | - | - | |
|
| 1.7131 | 26500 | 0.0421 | - | - | - | |
|
| 1.7196 | 26600 | 0.0416 | - | - | - | |
|
| 1.7260 | 26700 | 0.0422 | - | - | - | |
|
| 1.7325 | 26800 | 0.0418 | - | - | - | |
|
| 1.7390 | 26900 | 0.0425 | - | - | - | |
|
| 1.7454 | 27000 | 0.0421 | - | - | - | |
|
| 1.7519 | 27100 | 0.0421 | - | - | - | |
|
| 1.7584 | 27200 | 0.0418 | - | - | - | |
|
| 1.7648 | 27300 | 0.042 | - | - | - | |
|
| 1.7713 | 27400 | 0.0419 | - | - | - | |
|
| 1.7777 | 27500 | 0.0423 | - | - | - | |
|
| 1.7842 | 27600 | 0.0415 | - | - | - | |
|
| 1.7907 | 27700 | 0.0413 | - | - | - | |
|
| 1.7971 | 27800 | 0.0423 | - | - | - | |
|
| 1.8036 | 27900 | 0.0413 | - | - | - | |
|
| 1.8101 | 28000 | 0.0414 | - | - | - | |
|
| 1.8165 | 28100 | 0.0418 | - | - | - | |
|
| 1.8230 | 28200 | 0.0414 | - | - | - | |
|
| 1.8295 | 28300 | 0.0411 | - | - | - | |
|
| 1.8359 | 28400 | 0.0418 | - | - | - | |
|
| 1.8424 | 28500 | 0.0416 | - | - | - | |
|
| 1.8489 | 28600 | 0.0417 | - | - | - | |
|
| 1.8553 | 28700 | 0.041 | - | - | - | |
|
| 1.8618 | 28800 | 0.0413 | - | - | - | |
|
| 1.8683 | 28900 | 0.0409 | - | - | - | |
|
| 1.8747 | 29000 | 0.0413 | - | - | - | |
|
| 1.8812 | 29100 | 0.0413 | - | - | - | |
|
| 1.8876 | 29200 | 0.0411 | - | - | - | |
|
| 1.8941 | 29300 | 0.0408 | - | - | - | |
|
| 1.9006 | 29400 | 0.0415 | - | - | - | |
|
| 1.9070 | 29500 | 0.0415 | - | - | - | |
|
| 1.9135 | 29600 | 0.0408 | - | - | - | |
|
| 1.9200 | 29700 | 0.0407 | - | - | - | |
|
| 1.9264 | 29800 | 0.0409 | - | - | - | |
|
| 1.9329 | 29900 | 0.0414 | - | - | - | |
|
| 1.9394 | 30000 | 0.0409 | - | - | - | |
|
| 1.9458 | 30100 | 0.0407 | - | - | - | |
|
| 1.9523 | 30200 | 0.0404 | - | - | - | |
|
| 1.9588 | 30300 | 0.0408 | - | - | - | |
|
| 1.9652 | 30400 | 0.0409 | - | - | - | |
|
| 1.9717 | 30500 | 0.0409 | - | - | - | |
|
| 1.9781 | 30600 | 0.0408 | - | - | - | |
|
| 1.9846 | 30700 | 0.0403 | - | - | - | |
|
| 1.9911 | 30800 | 0.0403 | - | - | - | |
|
| 1.9975 | 30900 | 0.0405 | - | - | - | |
|
| 2.0 | 30938 | - | 0.0394 | -4.1528 | 0.9835 | |
|
| 2.0040 | 31000 | 0.0407 | - | - | - | |
|
| 2.0105 | 31100 | 0.0403 | - | - | - | |
|
| 2.0169 | 31200 | 0.0401 | - | - | - | |
|
| 2.0234 | 31300 | 0.0404 | - | - | - | |
|
| 2.0299 | 31400 | 0.0406 | - | - | - | |
|
| 2.0363 | 31500 | 0.0408 | - | - | - | |
|
| 2.0428 | 31600 | 0.0402 | - | - | - | |
|
| 2.0493 | 31700 | 0.0402 | - | - | - | |
|
| 2.0557 | 31800 | 0.0398 | - | - | - | |
|
| 2.0622 | 31900 | 0.0403 | - | - | - | |
|
| 2.0687 | 32000 | 0.0401 | - | - | - | |
|
| 2.0751 | 32100 | 0.0405 | - | - | - | |
|
| 2.0816 | 32200 | 0.0401 | - | - | - | |
|
| 2.0880 | 32300 | 0.04 | - | - | - | |
|
| 2.0945 | 32400 | 0.0399 | - | - | - | |
|
| 2.1010 | 32500 | 0.0398 | - | - | - | |
|
| 2.1074 | 32600 | 0.0406 | - | - | - | |
|
| 2.1139 | 32700 | 0.0397 | - | - | - | |
|
| 2.1204 | 32800 | 0.0403 | - | - | - | |
|
| 2.1268 | 32900 | 0.0399 | - | - | - | |
|
| 2.1333 | 33000 | 0.0401 | - | - | - | |
|
| 2.1398 | 33100 | 0.0401 | - | - | - | |
|
| 2.1462 | 33200 | 0.0403 | - | - | - | |
|
| 2.1527 | 33300 | 0.0399 | - | - | - | |
|
| 2.1592 | 33400 | 0.0398 | - | - | - | |
|
| 2.1656 | 33500 | 0.0399 | - | - | - | |
|
| 2.1721 | 33600 | 0.0398 | - | - | - | |
|
| 2.1786 | 33700 | 0.0395 | - | - | - | |
|
| 2.1850 | 33800 | 0.0395 | - | - | - | |
|
| 2.1915 | 33900 | 0.0396 | - | - | - | |
|
| 2.1979 | 34000 | 0.0399 | - | - | - | |
|
| 2.2044 | 34100 | 0.0398 | - | - | - | |
|
| 2.2109 | 34200 | 0.0393 | - | - | - | |
|
| 2.2173 | 34300 | 0.0393 | - | - | - | |
|
| 2.2238 | 34400 | 0.0399 | - | - | - | |
|
| 2.2303 | 34500 | 0.0393 | - | - | - | |
|
| 2.2367 | 34600 | 0.0398 | - | - | - | |
|
| 2.2432 | 34700 | 0.0394 | - | - | - | |
|
| 2.2497 | 34800 | 0.0392 | - | - | - | |
|
| 2.2561 | 34900 | 0.0397 | - | - | - | |
|
| 2.2626 | 35000 | 0.0399 | - | - | - | |
|
| 2.2691 | 35100 | 0.0393 | - | - | - | |
|
| 2.2755 | 35200 | 0.0394 | - | - | - | |
|
| 2.2820 | 35300 | 0.0389 | - | - | - | |
|
| 2.2884 | 35400 | 0.0392 | - | - | - | |
|
| 2.2949 | 35500 | 0.0393 | - | - | - | |
|
| 2.3014 | 35600 | 0.0393 | - | - | - | |
|
| 2.3078 | 35700 | 0.0393 | - | - | - | |
|
| 2.3143 | 35800 | 0.0391 | - | - | - | |
|
| 2.3208 | 35900 | 0.0389 | - | - | - | |
|
| 2.3272 | 36000 | 0.0398 | - | - | - | |
|
| 2.3337 | 36100 | 0.0394 | - | - | - | |
|
| 2.3402 | 36200 | 0.0389 | - | - | - | |
|
| 2.3466 | 36300 | 0.0388 | - | - | - | |
|
| 2.3531 | 36400 | 0.0392 | - | - | - | |
|
| 2.3596 | 36500 | 0.0386 | - | - | - | |
|
| 2.3660 | 36600 | 0.039 | - | - | - | |
|
| 2.3725 | 36700 | 0.0387 | - | - | - | |
|
| 2.3790 | 36800 | 0.0391 | - | - | - | |
|
| 2.3854 | 36900 | 0.0389 | - | - | - | |
|
| 2.3919 | 37000 | 0.0389 | - | - | - | |
|
| 2.3983 | 37100 | 0.0387 | - | - | - | |
|
| 2.4048 | 37200 | 0.0388 | - | - | - | |
|
| 2.4113 | 37300 | 0.0387 | - | - | - | |
|
| 2.4177 | 37400 | 0.0391 | - | - | - | |
|
| 2.4242 | 37500 | 0.039 | - | - | - | |
|
| 2.4307 | 37600 | 0.0384 | - | - | - | |
|
| 2.4371 | 37700 | 0.0388 | - | - | - | |
|
| 2.4436 | 37800 | 0.0385 | - | - | - | |
|
| 2.4501 | 37900 | 0.0388 | - | - | - | |
|
| 2.4565 | 38000 | 0.039 | - | - | - | |
|
| 2.4630 | 38100 | 0.0387 | - | - | - | |
|
| 2.4695 | 38200 | 0.0382 | - | - | - | |
|
| 2.4759 | 38300 | 0.0384 | - | - | - | |
|
| 2.4824 | 38400 | 0.0388 | - | - | - | |
|
| 2.4888 | 38500 | 0.0381 | - | - | - | |
|
| 2.4953 | 38600 | 0.0384 | - | - | - | |
|
| 2.5018 | 38700 | 0.0384 | - | - | - | |
|
| 2.5082 | 38800 | 0.0383 | - | - | - | |
|
| 2.5147 | 38900 | 0.0382 | - | - | - | |
|
| 2.5212 | 39000 | 0.0381 | - | - | - | |
|
| 2.5276 | 39100 | 0.0382 | - | - | - | |
|
| 2.5341 | 39200 | 0.0384 | - | - | - | |
|
| 2.5406 | 39300 | 0.0387 | - | - | - | |
|
| 2.5470 | 39400 | 0.0384 | - | - | - | |
|
| 2.5535 | 39500 | 0.0381 | - | - | - | |
|
| 2.5600 | 39600 | 0.038 | - | - | - | |
|
| 2.5664 | 39700 | 0.0384 | - | - | - | |
|
| 2.5729 | 39800 | 0.0379 | - | - | - | |
|
| 2.5794 | 39900 | 0.0385 | - | - | - | |
|
| 2.5858 | 40000 | 0.0381 | - | - | - | |
|
| 2.5923 | 40100 | 0.0382 | - | - | - | |
|
| 2.5987 | 40200 | 0.0377 | - | - | - | |
|
| 2.6052 | 40300 | 0.0375 | - | - | - | |
|
| 2.6117 | 40400 | 0.038 | - | - | - | |
|
| 2.6181 | 40500 | 0.0384 | - | - | - | |
|
| 2.6246 | 40600 | 0.0378 | - | - | - | |
|
| 2.6311 | 40700 | 0.0379 | - | - | - | |
|
| 2.6375 | 40800 | 0.0376 | - | - | - | |
|
| 2.6440 | 40900 | 0.0378 | - | - | - | |
|
| 2.6505 | 41000 | 0.0376 | - | - | - | |
|
| 2.6569 | 41100 | 0.0381 | - | - | - | |
|
| 2.6634 | 41200 | 0.0374 | - | - | - | |
|
| 2.6699 | 41300 | 0.0377 | - | - | - | |
|
| 2.6763 | 41400 | 0.038 | - | - | - | |
|
| 2.6828 | 41500 | 0.0377 | - | - | - | |
|
| 2.6892 | 41600 | 0.0379 | - | - | - | |
|
| 2.6957 | 41700 | 0.0377 | - | - | - | |
|
| 2.7022 | 41800 | 0.0373 | - | - | - | |
|
| 2.7086 | 41900 | 0.0374 | - | - | - | |
|
| 2.7151 | 42000 | 0.0373 | - | - | - | |
|
| 2.7216 | 42100 | 0.0374 | - | - | - | |
|
| 2.7280 | 42200 | 0.0375 | - | - | - | |
|
| 2.7345 | 42300 | 0.0375 | - | - | - | |
|
| 2.7410 | 42400 | 0.0379 | - | - | - | |
|
| 2.7474 | 42500 | 0.0379 | - | - | - | |
|
| 2.7539 | 42600 | 0.0378 | - | - | - | |
|
| 2.7604 | 42700 | 0.0375 | - | - | - | |
|
| 2.7668 | 42800 | 0.0375 | - | - | - | |
|
| 2.7733 | 42900 | 0.0377 | - | - | - | |
|
| 2.7798 | 43000 | 0.0378 | - | - | - | |
|
| 2.7862 | 43100 | 0.0372 | - | - | - | |
|
| 2.7927 | 43200 | 0.0374 | - | - | - | |
|
| 2.7991 | 43300 | 0.0376 | - | - | - | |
|
| 2.8056 | 43400 | 0.0374 | - | - | - | |
|
| 2.8121 | 43500 | 0.0371 | - | - | - | |
|
| 2.8185 | 43600 | 0.0377 | - | - | - | |
|
| 2.8250 | 43700 | 0.0368 | - | - | - | |
|
| 2.8315 | 43800 | 0.0376 | - | - | - | |
|
| 2.8379 | 43900 | 0.0374 | - | - | - | |
|
| 2.8444 | 44000 | 0.0378 | - | - | - | |
|
| 2.8509 | 44100 | 0.0375 | - | - | - | |
|
| 2.8573 | 44200 | 0.0371 | - | - | - | |
|
| 2.8638 | 44300 | 0.037 | - | - | - | |
|
| 2.8703 | 44400 | 0.0371 | - | - | - | |
|
| 2.8767 | 44500 | 0.0374 | - | - | - | |
|
| 2.8832 | 44600 | 0.037 | - | - | - | |
|
| 2.8897 | 44700 | 0.0374 | - | - | - | |
|
| 2.8961 | 44800 | 0.0368 | - | - | - | |
|
| 2.9026 | 44900 | 0.0377 | - | - | - | |
|
| 2.9090 | 45000 | 0.0375 | - | - | - | |
|
| 2.9155 | 45100 | 0.0367 | - | - | - | |
|
| 2.9220 | 45200 | 0.0368 | - | - | - | |
|
| 2.9284 | 45300 | 0.0372 | - | - | - | |
|
| 2.9349 | 45400 | 0.0374 | - | - | - | |
|
| 2.9414 | 45500 | 0.0367 | - | - | - | |
|
| 2.9478 | 45600 | 0.037 | - | - | - | |
|
| 2.9543 | 45700 | 0.0368 | - | - | - | |
|
| 2.9608 | 45800 | 0.0367 | - | - | - | |
|
| 2.9672 | 45900 | 0.0372 | - | - | - | |
|
| 2.9737 | 46000 | 0.0375 | - | - | - | |
|
| 2.9802 | 46100 | 0.0368 | - | - | - | |
|
| 2.9866 | 46200 | 0.0368 | - | - | - | |
|
| 2.9931 | 46300 | 0.0367 | - | - | - | |
|
| 2.9995 | 46400 | 0.0366 | - | - | - | |
|
| 3.0 | 46407 | - | 0.0357 | -3.7998 | 0.9869 | |
|
| 3.0060 | 46500 | 0.0372 | - | - | - | |
|
| 3.0125 | 46600 | 0.0365 | - | - | - | |
|
| 3.0189 | 46700 | 0.0369 | - | - | - | |
|
| 3.0254 | 46800 | 0.0368 | - | - | - | |
|
| 3.0319 | 46900 | 0.037 | - | - | - | |
|
| 3.0383 | 47000 | 0.037 | - | - | - | |
|
| 3.0448 | 47100 | 0.0367 | - | - | - | |
|
| 3.0513 | 47200 | 0.0364 | - | - | - | |
|
| 3.0577 | 47300 | 0.0366 | - | - | - | |
|
| 3.0642 | 47400 | 0.0366 | - | - | - | |
|
| 3.0707 | 47500 | 0.0371 | - | - | - | |
|
| 3.0771 | 47600 | 0.0367 | - | - | - | |
|
| 3.0836 | 47700 | 0.0368 | - | - | - | |
|
| 3.0901 | 47800 | 0.0366 | - | - | - | |
|
| 3.0965 | 47900 | 0.0362 | - | - | - | |
|
| 3.1030 | 48000 | 0.0368 | - | - | - | |
|
| 3.1094 | 48100 | 0.0366 | - | - | - | |
|
| 3.1159 | 48200 | 0.0367 | - | - | - | |
|
| 3.1224 | 48300 | 0.0369 | - | - | - | |
|
| 3.1288 | 48400 | 0.0366 | - | - | - | |
|
| 3.1353 | 48500 | 0.0366 | - | - | - | |
|
| 3.1418 | 48600 | 0.0367 | - | - | - | |
|
| 3.1482 | 48700 | 0.037 | - | - | - | |
|
| 3.1547 | 48800 | 0.0367 | - | - | - | |
|
| 3.1612 | 48900 | 0.0362 | - | - | - | |
|
| 3.1676 | 49000 | 0.0367 | - | - | - | |
|
| 3.1741 | 49100 | 0.0365 | - | - | - | |
|
| 3.1806 | 49200 | 0.0363 | - | - | - | |
|
| 3.1870 | 49300 | 0.036 | - | - | - | |
|
| 3.1935 | 49400 | 0.0366 | - | - | - | |
|
| 3.1999 | 49500 | 0.0366 | - | - | - | |
|
| 3.2064 | 49600 | 0.0366 | - | - | - | |
|
| 3.2129 | 49700 | 0.0361 | - | - | - | |
|
| 3.2193 | 49800 | 0.0365 | - | - | - | |
|
| 3.2258 | 49900 | 0.0365 | - | - | - | |
|
| 3.2323 | 50000 | 0.0361 | - | - | - | |
|
| 3.2387 | 50100 | 0.0365 | - | - | - | |
|
| 3.2452 | 50200 | 0.0363 | - | - | - | |
|
| 3.2517 | 50300 | 0.0362 | - | - | - | |
|
| 3.2581 | 50400 | 0.0366 | - | - | - | |
|
| 3.2646 | 50500 | 0.0366 | - | - | - | |
|
| 3.2711 | 50600 | 0.0367 | - | - | - | |
|
| 3.2775 | 50700 | 0.0361 | - | - | - | |
|
| 3.2840 | 50800 | 0.0359 | - | - | - | |
|
| 3.2905 | 50900 | 0.0363 | - | - | - | |
|
| 3.2969 | 51000 | 0.0361 | - | - | - | |
|
| 3.3034 | 51100 | 0.0364 | - | - | - | |
|
| 3.3098 | 51200 | 0.0363 | - | - | - | |
|
| 3.3163 | 51300 | 0.0362 | - | - | - | |
|
| 3.3228 | 51400 | 0.0359 | - | - | - | |
|
| 3.3292 | 51500 | 0.0368 | - | - | - | |
|
| 3.3357 | 51600 | 0.0361 | - | - | - | |
|
| 3.3422 | 51700 | 0.0359 | - | - | - | |
|
| 3.3486 | 51800 | 0.0362 | - | - | - | |
|
| 3.3551 | 51900 | 0.0363 | - | - | - | |
|
| 3.3616 | 52000 | 0.0357 | - | - | - | |
|
| 3.3680 | 52100 | 0.0358 | - | - | - | |
|
| 3.3745 | 52200 | 0.036 | - | - | - | |
|
| 3.3810 | 52300 | 0.0365 | - | - | - | |
|
| 3.3874 | 52400 | 0.0359 | - | - | - | |
|
| 3.3939 | 52500 | 0.0359 | - | - | - | |
|
| 3.4003 | 52600 | 0.0362 | - | - | - | |
|
| 3.4068 | 52700 | 0.0358 | - | - | - | |
|
| 3.4133 | 52800 | 0.036 | - | - | - | |
|
| 3.4197 | 52900 | 0.0366 | - | - | - | |
|
| 3.4262 | 53000 | 0.036 | - | - | - | |
|
| 3.4327 | 53100 | 0.0357 | - | - | - | |
|
| 3.4391 | 53200 | 0.036 | - | - | - | |
|
| 3.4456 | 53300 | 0.036 | - | - | - | |
|
| 3.4521 | 53400 | 0.036 | - | - | - | |
|
| 3.4585 | 53500 | 0.0364 | - | - | - | |
|
| 3.4650 | 53600 | 0.0359 | - | - | - | |
|
| 3.4715 | 53700 | 0.0354 | - | - | - | |
|
| 3.4779 | 53800 | 0.0359 | - | - | - | |
|
| 3.4844 | 53900 | 0.036 | - | - | - | |
|
| 3.4909 | 54000 | 0.0355 | - | - | - | |
|
| 3.4973 | 54100 | 0.0358 | - | - | - | |
|
| 3.5038 | 54200 | 0.0355 | - | - | - | |
|
| 3.5102 | 54300 | 0.036 | - | - | - | |
|
| 3.5167 | 54400 | 0.0354 | - | - | - | |
|
| 3.5232 | 54500 | 0.0357 | - | - | - | |
|
| 3.5296 | 54600 | 0.0356 | - | - | - | |
|
| 3.5361 | 54700 | 0.036 | - | - | - | |
|
| 3.5426 | 54800 | 0.036 | - | - | - | |
|
| 3.5490 | 54900 | 0.0358 | - | - | - | |
|
| 3.5555 | 55000 | 0.0356 | - | - | - | |
|
| 3.5620 | 55100 | 0.0357 | - | - | - | |
|
| 3.5684 | 55200 | 0.0356 | - | - | - | |
|
| 3.5749 | 55300 | 0.0358 | - | - | - | |
|
| 3.5814 | 55400 | 0.036 | - | - | - | |
|
| 3.5878 | 55500 | 0.0356 | - | - | - | |
|
| 3.5943 | 55600 | 0.0358 | - | - | - | |
|
| 3.6007 | 55700 | 0.0351 | - | - | - | |
|
| 3.6072 | 55800 | 0.0352 | - | - | - | |
|
| 3.6137 | 55900 | 0.0357 | - | - | - | |
|
| 3.6201 | 56000 | 0.0359 | - | - | - | |
|
| 3.6266 | 56100 | 0.035 | - | - | - | |
|
| 3.6331 | 56200 | 0.0357 | - | - | - | |
|
| 3.6395 | 56300 | 0.0354 | - | - | - | |
|
| 3.6460 | 56400 | 0.0352 | - | - | - | |
|
| 3.6525 | 56500 | 0.0356 | - | - | - | |
|
| 3.6589 | 56600 | 0.0356 | - | - | - | |
|
| 3.6654 | 56700 | 0.0349 | - | - | - | |
|
| 3.6719 | 56800 | 0.0358 | - | - | - | |
|
| 3.6783 | 56900 | 0.0355 | - | - | - | |
|
| 3.6848 | 57000 | 0.0353 | - | - | - | |
|
| 3.6913 | 57100 | 0.0355 | - | - | - | |
|
| 3.6977 | 57200 | 0.0353 | - | - | - | |
|
| 3.7042 | 57300 | 0.035 | - | - | - | |
|
| 3.7106 | 57400 | 0.0351 | - | - | - | |
|
| 3.7171 | 57500 | 0.035 | - | - | - | |
|
| 3.7236 | 57600 | 0.0353 | - | - | - | |
|
| 3.7300 | 57700 | 0.0353 | - | - | - | |
|
| 3.7365 | 57800 | 0.0356 | - | - | - | |
|
| 3.7430 | 57900 | 0.0356 | - | - | - | |
|
| 3.7494 | 58000 | 0.0355 | - | - | - | |
|
| 3.7559 | 58100 | 0.0355 | - | - | - | |
|
| 3.7624 | 58200 | 0.0354 | - | - | - | |
|
| 3.7688 | 58300 | 0.0353 | - | - | - | |
|
| 3.7753 | 58400 | 0.0357 | - | - | - | |
|
| 3.7818 | 58500 | 0.0353 | - | - | - | |
|
| 3.7882 | 58600 | 0.035 | - | - | - | |
|
| 3.7947 | 58700 | 0.0355 | - | - | - | |
|
| 3.8012 | 58800 | 0.035 | - | - | - | |
|
| 3.8076 | 58900 | 0.0355 | - | - | - | |
|
| 3.8141 | 59000 | 0.0351 | - | - | - | |
|
| 3.8205 | 59100 | 0.0353 | - | - | - | |
|
| 3.8270 | 59200 | 0.0349 | - | - | - | |
|
| 3.8335 | 59300 | 0.0355 | - | - | - | |
|
| 3.8399 | 59400 | 0.0353 | - | - | - | |
|
| 3.8464 | 59500 | 0.0357 | - | - | - | |
|
| 3.8529 | 59600 | 0.0351 | - | - | - | |
|
| 3.8593 | 59700 | 0.0351 | - | - | - | |
|
| 3.8658 | 59800 | 0.0352 | - | - | - | |
|
| 3.8723 | 59900 | 0.035 | - | - | - | |
|
| 3.8787 | 60000 | 0.0353 | - | - | - | |
|
| 3.8852 | 60100 | 0.0351 | - | - | - | |
|
| 3.8917 | 60200 | 0.0352 | - | - | - | |
|
| 3.8981 | 60300 | 0.0351 | - | - | - | |
|
| 3.9046 | 60400 | 0.0356 | - | - | - | |
|
| 3.9110 | 60500 | 0.0352 | - | - | - | |
|
| 3.9175 | 60600 | 0.0347 | - | - | - | |
|
| 3.9240 | 60700 | 0.035 | - | - | - | |
|
| 3.9304 | 60800 | 0.0352 | - | - | - | |
|
| 3.9369 | 60900 | 0.0356 | - | - | - | |
|
| 3.9434 | 61000 | 0.0346 | - | - | - | |
|
| 3.9498 | 61100 | 0.0352 | - | - | - | |
|
| 3.9563 | 61200 | 0.0349 | - | - | - | |
|
| 3.9628 | 61300 | 0.0349 | - | - | - | |
|
| 3.9692 | 61400 | 0.0354 | - | - | - | |
|
| 3.9757 | 61500 | 0.0354 | - | - | - | |
|
| 3.9822 | 61600 | 0.0348 | - | - | - | |
|
| 3.9886 | 61700 | 0.0349 | - | - | - | |
|
| 3.9951 | 61800 | 0.0347 | - | - | - | |
|
| 4.0 | 61876 | - | 0.0339 | -3.6284 | 0.9876 | |
|
| 4.0016 | 61900 | 0.0351 | - | - | - | |
|
| 4.0080 | 62000 | 0.035 | - | - | - | |
|
| 4.0145 | 62100 | 0.0348 | - | - | - | |
|
| 4.0209 | 62200 | 0.0349 | - | - | - | |
|
| 4.0274 | 62300 | 0.0352 | - | - | - | |
|
| 4.0339 | 62400 | 0.0351 | - | - | - | |
|
| 4.0403 | 62500 | 0.0352 | - | - | - | |
|
| 4.0468 | 62600 | 0.0347 | - | - | - | |
|
| 4.0533 | 62700 | 0.0347 | - | - | - | |
|
| 4.0597 | 62800 | 0.0348 | - | - | - | |
|
| 4.0662 | 62900 | 0.035 | - | - | - | |
|
| 4.0727 | 63000 | 0.035 | - | - | - | |
|
| 4.0791 | 63100 | 0.0349 | - | - | - | |
|
| 4.0856 | 63200 | 0.035 | - | - | - | |
|
| 4.0921 | 63300 | 0.0349 | - | - | - | |
|
| 4.0985 | 63400 | 0.0346 | - | - | - | |
|
| 4.1050 | 63500 | 0.035 | - | - | - | |
|
| 4.1114 | 63600 | 0.0347 | - | - | - | |
|
| 4.1179 | 63700 | 0.0351 | - | - | - | |
|
| 4.1244 | 63800 | 0.0351 | - | - | - | |
|
| 4.1308 | 63900 | 0.035 | - | - | - | |
|
| 4.1373 | 64000 | 0.0349 | - | - | - | |
|
| 4.1438 | 64100 | 0.0352 | - | - | - | |
|
| 4.1502 | 64200 | 0.0351 | - | - | - | |
|
| 4.1567 | 64300 | 0.0348 | - | - | - | |
|
| 4.1632 | 64400 | 0.0347 | - | - | - | |
|
| 4.1696 | 64500 | 0.0352 | - | - | - | |
|
| 4.1761 | 64600 | 0.0346 | - | - | - | |
|
| 4.1826 | 64700 | 0.0345 | - | - | - | |
|
| 4.1890 | 64800 | 0.0346 | - | - | - | |
|
| 4.1955 | 64900 | 0.0351 | - | - | - | |
|
| 4.2020 | 65000 | 0.0348 | - | - | - | |
|
| 4.2084 | 65100 | 0.035 | - | - | - | |
|
| 4.2149 | 65200 | 0.0345 | - | - | - | |
|
| 4.2213 | 65300 | 0.0349 | - | - | - | |
|
| 4.2278 | 65400 | 0.0351 | - | - | - | |
|
| 4.2343 | 65500 | 0.0345 | - | - | - | |
|
| 4.2407 | 65600 | 0.035 | - | - | - | |
|
| 4.2472 | 65700 | 0.0346 | - | - | - | |
|
| 4.2537 | 65800 | 0.0347 | - | - | - | |
|
| 4.2601 | 65900 | 0.0351 | - | - | - | |
|
| 4.2666 | 66000 | 0.0347 | - | - | - | |
|
| 4.2731 | 66100 | 0.0354 | - | - | - | |
|
| 4.2795 | 66200 | 0.0342 | - | - | - | |
|
| 4.2860 | 66300 | 0.0345 | - | - | - | |
|
| 4.2925 | 66400 | 0.0349 | - | - | - | |
|
| 4.2989 | 66500 | 0.0347 | - | - | - | |
|
| 4.3054 | 66600 | 0.0347 | - | - | - | |
|
| 4.3118 | 66700 | 0.0348 | - | - | - | |
|
| 4.3183 | 66800 | 0.0347 | - | - | - | |
|
| 4.3248 | 66900 | 0.0346 | - | - | - | |
|
| 4.3312 | 67000 | 0.0353 | - | - | - | |
|
| 4.3377 | 67100 | 0.0345 | - | - | - | |
|
| 4.3442 | 67200 | 0.0343 | - | - | - | |
|
| 4.3506 | 67300 | 0.035 | - | - | - | |
|
| 4.3571 | 67400 | 0.0346 | - | - | - | |
|
| 4.3636 | 67500 | 0.0343 | - | - | - | |
|
| 4.3700 | 67600 | 0.0344 | - | - | - | |
|
| 4.3765 | 67700 | 0.0348 | - | - | - | |
|
| 4.3830 | 67800 | 0.0348 | - | - | - | |
|
| 4.3894 | 67900 | 0.0345 | - | - | - | |
|
| 4.3959 | 68000 | 0.0347 | - | - | - | |
|
| 4.4024 | 68100 | 0.0345 | - | - | - | |
|
| 4.4088 | 68200 | 0.0346 | - | - | - | |
|
| 4.4153 | 68300 | 0.0349 | - | - | - | |
|
| 4.4217 | 68400 | 0.0349 | - | - | - | |
|
| 4.4282 | 68500 | 0.0345 | - | - | - | |
|
| 4.4347 | 68600 | 0.0346 | - | - | - | |
|
| 4.4411 | 68700 | 0.0345 | - | - | - | |
|
| 4.4476 | 68800 | 0.0347 | - | - | - | |
|
| 4.4541 | 68900 | 0.0346 | - | - | - | |
|
| 4.4605 | 69000 | 0.035 | - | - | - | |
|
| 4.4670 | 69100 | 0.0343 | - | - | - | |
|
| 4.4735 | 69200 | 0.0346 | - | - | - | |
|
| 4.4799 | 69300 | 0.0346 | - | - | - | |
|
| 4.4864 | 69400 | 0.0346 | - | - | - | |
|
| 4.4929 | 69500 | 0.0342 | - | - | - | |
|
| 4.4993 | 69600 | 0.0346 | - | - | - | |
|
| 4.5058 | 69700 | 0.0342 | - | - | - | |
|
| 4.5123 | 69800 | 0.0348 | - | - | - | |
|
| 4.5187 | 69900 | 0.0341 | - | - | - | |
|
| 4.5252 | 70000 | 0.0344 | - | - | - | |
|
| 4.5316 | 70100 | 0.0345 | - | - | - | |
|
| 4.5381 | 70200 | 0.0348 | - | - | - | |
|
| 4.5446 | 70300 | 0.0349 | - | - | - | |
|
| 4.5510 | 70400 | 0.0344 | - | - | - | |
|
| 4.5575 | 70500 | 0.0342 | - | - | - | |
|
| 4.5640 | 70600 | 0.0346 | - | - | - | |
|
| 4.5704 | 70700 | 0.0342 | - | - | - | |
|
| 4.5769 | 70800 | 0.0345 | - | - | - | |
|
| 4.5834 | 70900 | 0.0347 | - | - | - | |
|
| 4.5898 | 71000 | 0.0345 | - | - | - | |
|
| 4.5963 | 71100 | 0.0343 | - | - | - | |
|
| 4.6028 | 71200 | 0.0341 | - | - | - | |
|
| 4.6092 | 71300 | 0.0341 | - | - | - | |
|
| 4.6157 | 71400 | 0.0347 | - | - | - | |
|
| 4.6221 | 71500 | 0.0347 | - | - | - | |
|
| 4.6286 | 71600 | 0.0339 | - | - | - | |
|
| 4.6351 | 71700 | 0.0344 | - | - | - | |
|
| 4.6415 | 71800 | 0.0342 | - | - | - | |
|
| 4.6480 | 71900 | 0.0342 | - | - | - | |
|
| 4.6545 | 72000 | 0.0346 | - | - | - | |
|
| 4.6609 | 72100 | 0.0342 | - | - | - | |
|
| 4.6674 | 72200 | 0.0341 | - | - | - | |
|
| 4.6739 | 72300 | 0.0344 | - | - | - | |
|
| 4.6803 | 72400 | 0.0345 | - | - | - | |
|
| 4.6868 | 72500 | 0.0345 | - | - | - | |
|
| 4.6933 | 72600 | 0.0342 | - | - | - | |
|
| 4.6997 | 72700 | 0.0341 | - | - | - | |
|
| 4.7062 | 72800 | 0.034 | - | - | - | |
|
| 4.7127 | 72900 | 0.0343 | - | - | - | |
|
| 4.7191 | 73000 | 0.0337 | - | - | - | |
|
| 4.7256 | 73100 | 0.0343 | - | - | - | |
|
| 4.7320 | 73200 | 0.0343 | - | - | - | |
|
| 4.7385 | 73300 | 0.0346 | - | - | - | |
|
| 4.7450 | 73400 | 0.0346 | - | - | - | |
|
| 4.7514 | 73500 | 0.0345 | - | - | - | |
|
| 4.7579 | 73600 | 0.0343 | - | - | - | |
|
| 4.7644 | 73700 | 0.0344 | - | - | - | |
|
| 4.7708 | 73800 | 0.0345 | - | - | - | |
|
| 4.7773 | 73900 | 0.0347 | - | - | - | |
|
| 4.7838 | 74000 | 0.034 | - | - | - | |
|
| 4.7902 | 74100 | 0.034 | - | - | - | |
|
| 4.7967 | 74200 | 0.0348 | - | - | - | |
|
| 4.8032 | 74300 | 0.0338 | - | - | - | |
|
| 4.8096 | 74400 | 0.0346 | - | - | - | |
|
| 4.8161 | 74500 | 0.0344 | - | - | - | |
|
| 4.8225 | 74600 | 0.0342 | - | - | - | |
|
| 4.8290 | 74700 | 0.034 | - | - | - | |
|
| 4.8355 | 74800 | 0.0346 | - | - | - | |
|
| 4.8419 | 74900 | 0.0346 | - | - | - | |
|
| 4.8484 | 75000 | 0.0346 | - | - | - | |
|
| 4.8549 | 75100 | 0.034 | - | - | - | |
|
| 4.8613 | 75200 | 0.0343 | - | - | - | |
|
| 4.8678 | 75300 | 0.034 | - | - | - | |
|
| 4.8743 | 75400 | 0.0344 | - | - | - | |
|
| 4.8807 | 75500 | 0.0344 | - | - | - | |
|
| 4.8872 | 75600 | 0.0342 | - | - | - | |
|
| 4.8937 | 75700 | 0.0341 | - | - | - | |
|
| 4.9001 | 75800 | 0.0345 | - | - | - | |
|
| 4.9066 | 75900 | 0.0347 | - | - | - | |
|
| 4.9131 | 76000 | 0.0341 | - | - | - | |
|
| 4.9195 | 76100 | 0.0339 | - | - | - | |
|
| 4.9260 | 76200 | 0.0343 | - | - | - | |
|
| 4.9324 | 76300 | 0.0346 | - | - | - | |
|
| 4.9389 | 76400 | 0.0344 | - | - | - | |
|
| 4.9454 | 76500 | 0.0341 | - | - | - | |
|
| 4.9518 | 76600 | 0.034 | - | - | - | |
|
| 4.9583 | 76700 | 0.0342 | - | - | - | |
|
| 4.9648 | 76800 | 0.0344 | - | - | - | |
|
| 4.9712 | 76900 | 0.0344 | - | - | - | |
|
| 4.9777 | 77000 | 0.0343 | - | - | - | |
|
| 4.9842 | 77100 | 0.0341 | - | - | - | |
|
| 4.9906 | 77200 | 0.0341 | - | - | - | |
|
| 4.9971 | 77300 | 0.0342 | - | - | - | |
|
| 5.0 | 77345 | - | 0.0331 | -3.5554 | 0.9877 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.11.9 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.46.3 |
|
- PyTorch: 2.4.0 |
|
- Accelerate: 1.1.1 |
|
- Datasets: 3.1.0 |
|
- Tokenizers: 0.20.3 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MSELoss |
|
```bibtex |
|
@inproceedings{reimers-2020-multilingual-sentence-bert, |
|
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2020", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/2004.09813", |
|
} |
|
``` |
|
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