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yahyaabd
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allstats-semantic-search-model-v1-3

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:212940
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
yahyaabd commited on
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Add new SentenceTransformer model

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Files changed (11) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +534 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +62 -0
.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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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:212940
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+ - loss:CosineSimilarityLoss
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+ base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+ widget:
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+ - source_sentence: Ringkasan data strategis BPS 2012
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+ sentences:
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+ - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan
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+ Jenis Pekerjaan Utama, 2021
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+ - Laporan Perekonomian Indonesia 2007
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+ - Statistik Potensi Desa Provinsi Banten 2008
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+ - source_sentence: tahun berapa ekspor naik 2,37% dan impor naik 30,30%?
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+ sentences:
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+ - Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %
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+ - Indeks Harga Konsumen per Kelompok di 82 Kota <sup>1</sup> (2012=100)
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+ - 'Februari 2022: Tingkat Pengangguran Terbuka (TPT) sebesar 5,83 persen dan Rata-rata
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+ upah buruh sebesar 2,89 juta rupiah per bulan'
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+ - source_sentence: akses air bersih di indonesia (2005-2009)
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+ sentences:
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+ - Desember 2016, Rupiah Terapresiasi 0,74 Persen Terhadap Dolar Amerika
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+ - Statistik Air Bersih 2005-2009
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+ - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
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+ yang Ditamatkan dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018
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+ - source_sentence: Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2014-2018, Buku
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+ 2 Pulau Jawa dan Bali
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+ sentences:
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+ - Profil Migran Hasil Susenas 2011-2012
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+ - Statistik Gas Kota 2004-2008
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+ - Jumlah kunjungan wisman ke Indonesia melalui pintu masuk utama pada Juni 2022
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+ mencapai 345,44 ribu kunjungan dan Jumlah penumpang angkutan udara internasional
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+ pada Juni 2022 naik 23,28 persen
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+ - source_sentence: perubahan nilai tukar petani bulan mei 2017
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+ sentences:
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+ - Perkembangan Nilai Tukar Petani Mei 2017
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+ - NTP Naik 0,15%, Harga Gabah Kualitas GKG Naik 0,98%
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+ - Statistik Restoran/Rumah Makan Tahun 2014
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+ datasets:
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+ - yahyaabd/allstats-semantic-search-synthetic-dataset-v1
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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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+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: allstats semantic search v1 3 dev
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+ type: allstats-semantic-search-v1-3-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9958745183830993
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.96406478662103
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+ name: Spearman Cosine
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: allstat semantic search v1 3 test
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+ type: allstat-semantic-search-v1-3-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9960950217535739
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.9647914507837114
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) 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:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
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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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+ - [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1)
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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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+
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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: XLMRobertaModel
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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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+
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+ ## Usage
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+
114
+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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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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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("yahyaabd/allstats-semantic-search-model-v1-3")
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+ # Run inference
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+ sentences = [
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+ 'perubahan nilai tukar petani bulan mei 2017',
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+ 'Perkembangan Nilai Tukar Petani Mei 2017',
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+ 'Statistik Restoran/Rumah Makan Tahun 2014',
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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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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
172
+ #### Semantic Similarity
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+
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+ * Datasets: `allstats-semantic-search-v1-3-dev` and `allstat-semantic-search-v1-3-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | allstats-semantic-search-v1-3-dev | allstat-semantic-search-v1-3-test |
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+ |:--------------------|:----------------------------------|:----------------------------------|
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+ | pearson_cosine | 0.9959 | 0.9961 |
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+ | **spearman_cosine** | **0.9641** | **0.9648** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### allstats-semantic-search-synthetic-dataset-v1
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+
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+ * Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
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+ * Size: 212,940 training samples
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+ * Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | query | doc | label |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 11.46 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.47 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.05</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:---------------------------------------------------------------|:-----------------------------------------------------------------------|:------------------|
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+ | <code>aDta industri besar dan sedang Indonesia 2008</code> | <code>Statistik Industri Besar dan Sedang Indonesia 2008</code> | <code>0.9</code> |
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+ | <code>profil bisnis konstruksi individu jawa barat 2022</code> | <code>Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku</code> | <code>0.15</code> |
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+ | <code>data statistik ekonomi indonesia</code> | <code>Nilai Tukar Valuta Asing di Indonesia 2014</code> | <code>0.08</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
216
+ {
217
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
218
+ }
219
+ ```
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+
221
+ ### Evaluation Dataset
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+
223
+ #### allstats-semantic-search-synthetic-dataset-v1
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+
225
+ * Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
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+ * Size: 26,618 evaluation samples
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+ * Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | query | doc | label |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 11.38 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.63 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:-------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------|
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+ | <code>tahun berapa ekspor naik 2,37% dan impor naik 30,30%?</code> | <code>Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %</code> | <code>1.0</code> |
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+ | <code>Berapa produksi padi pada tahun 2023?</code> | <code>Produksi padi tahun lainnya</code> | <code>0.0</code> |
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+ | <code>data statistik solus per aqua 2015</code> | <code>Statistik Solus Per Aqua (SPA) 2015</code> | <code>0.97</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
242
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
243
+ }
244
+ ```
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+
246
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
249
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `num_train_epochs`: 16
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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`: steps
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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`: 5e-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`: 16
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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
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: None
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `include_for_metrics`: []
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
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+ - `average_tokens_across_devices`: False
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+ - `prompts`: None
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
375
+ </details>
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+
377
+ ### Training Logs
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+ <details><summary>Click to expand</summary>
379
+
380
+ | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-3-dev_spearman_cosine | allstat-semantic-search-v1-3-test_spearman_cosine |
381
+ |:-------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-------------------------------------------------:|
382
+ | 0.1502 | 500 | 0.0579 | 0.0351 | 0.7132 | - |
383
+ | 0.3005 | 1000 | 0.03 | 0.0225 | 0.7589 | - |
384
+ | 0.4507 | 1500 | 0.0219 | 0.0185 | 0.7834 | - |
385
+ | 0.6010 | 2000 | 0.0181 | 0.0163 | 0.7946 | - |
386
+ | 0.7512 | 2500 | 0.0162 | 0.0147 | 0.7941 | - |
387
+ | 0.9014 | 3000 | 0.015 | 0.0147 | 0.8050 | - |
388
+ | 1.0517 | 3500 | 0.014 | 0.0131 | 0.7946 | - |
389
+ | 1.2019 | 4000 | 0.0119 | 0.0126 | 0.8038 | - |
390
+ | 1.3522 | 4500 | 0.0121 | 0.0128 | 0.8213 | - |
391
+ | 1.5024 | 5000 | 0.0117 | 0.0116 | 0.8268 | - |
392
+ | 1.6526 | 5500 | 0.0124 | 0.0117 | 0.8269 | - |
393
+ | 1.8029 | 6000 | 0.0111 | 0.0109 | 0.8421 | - |
394
+ | 1.9531 | 6500 | 0.0105 | 0.0108 | 0.8278 | - |
395
+ | 2.1034 | 7000 | 0.0091 | 0.0093 | 0.8460 | - |
396
+ | 2.2536 | 7500 | 0.0085 | 0.0091 | 0.8469 | - |
397
+ | 2.4038 | 8000 | 0.0079 | 0.0083 | 0.8595 | - |
398
+ | 2.5541 | 8500 | 0.0075 | 0.0085 | 0.8495 | - |
399
+ | 2.7043 | 9000 | 0.0073 | 0.0082 | 0.8614 | - |
400
+ | 2.8546 | 9500 | 0.0068 | 0.0077 | 0.8696 | - |
401
+ | 3.0048 | 10000 | 0.0066 | 0.0076 | 0.8669 | - |
402
+ | 3.1550 | 10500 | 0.0058 | 0.0072 | 0.8678 | - |
403
+ | 3.3053 | 11000 | 0.0056 | 0.0067 | 0.8703 | - |
404
+ | 3.4555 | 11500 | 0.0054 | 0.0067 | 0.8766 | - |
405
+ | 3.6058 | 12000 | 0.0054 | 0.0063 | 0.8678 | - |
406
+ | 3.7560 | 12500 | 0.0051 | 0.0061 | 0.8786 | - |
407
+ | 3.9062 | 13000 | 0.0052 | 0.0077 | 0.8699 | - |
408
+ | 4.0565 | 13500 | 0.005 | 0.0055 | 0.8859 | - |
409
+ | 4.2067 | 14000 | 0.0041 | 0.0054 | 0.8900 | - |
410
+ | 4.3570 | 14500 | 0.0038 | 0.0052 | 0.8892 | - |
411
+ | 4.5072 | 15000 | 0.0039 | 0.0050 | 0.8895 | - |
412
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413
+ | 4.8077 | 16000 | 0.0042 | 0.0051 | 0.8927 | - |
414
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415
+ | 5.1082 | 17000 | 0.0034 | 0.0053 | 0.8998 | - |
416
+ | 5.2584 | 17500 | 0.003 | 0.0047 | 0.9023 | - |
417
+ | 5.4087 | 18000 | 0.0032 | 0.0045 | 0.9039 | - |
418
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419
+ | 5.7091 | 19000 | 0.0032 | 0.0041 | 0.9085 | - |
420
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421
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422
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423
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424
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425
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429
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458
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470
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476
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477
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478
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479
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480
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481
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482
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483
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484
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487
+ | 15.9255 | 53000 | 0.0004 | 0.0015 | 0.9641 | - |
488
+ | 16.0 | 53248 | - | - | - | 0.9648 |
489
+
490
+ </details>
491
+
492
+ ### Framework Versions
493
+ - Python: 3.10.12
494
+ - Sentence Transformers: 3.3.1
495
+ - Transformers: 4.47.1
496
+ - PyTorch: 2.2.2+cu121
497
+ - Accelerate: 1.2.1
498
+ - Datasets: 3.2.0
499
+ - Tokenizers: 0.21.0
500
+
501
+ ## Citation
502
+
503
+ ### BibTeX
504
+
505
+ #### Sentence Transformers
506
+ ```bibtex
507
+ @inproceedings{reimers-2019-sentence-bert,
508
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
509
+ author = "Reimers, Nils and Gurevych, Iryna",
510
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
511
+ month = "11",
512
+ year = "2019",
513
+ publisher = "Association for Computational Linguistics",
514
+ url = "https://arxiv.org/abs/1908.10084",
515
+ }
516
+ ```
517
+
518
+ <!--
519
+ ## Glossary
520
+
521
+ *Clearly define terms in order to be accessible across audiences.*
522
+ -->
523
+
524
+ <!--
525
+ ## Model Card Authors
526
+
527
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
528
+ -->
529
+
530
+ <!--
531
+ ## Model Card Contact
532
+
533
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
534
+ -->
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