Duplicate from universalml0/finetuned_embedding_model_e5-large-multilingual-large

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "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
+}

README.md

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+---
+base_model: intfloat/multilingual-e5-large-instruct
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:45199
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: प्रधानमन्त्री नरेन्द्र मोदी सरकारका असफलताहरू के के हुन्?
+  sentences:
+  - पूर्वोत्तर राज्यहरूका मुख्य समस्याहरू के के हुन् र तिनीहरूको केन्द्रीय सरकारसँग
+    असन्तोष के हो?
+  - पूर्णांक के हो?
+  - नरेन्द्र मोदी सरकारले कुन क्षेत्रमा असफल भएको छ?
+- source_sentence: 'मैले विचार गर्नुपर्ने कलेजहरू के के हुन्, विचार गर्नुपर्ने कारकहरू:
+    केएमसी म्यानिपल वा केएमसी मंगोलमा?'
+  sentences:
+  - मंगलोर शान्त वा हिंस्रक स्थान हो?
+  - पुरुषहरूको तुलनामा महिलाहरूको लागि यौनिक आनन्द बढी हुन्छ कि हुँदैन?
+  - के कसैले केएमसी मानिपाल र मंगलोरको संक्षिप्त तुलना गर्न सक्छ?
+- source_sentence: म कसरी मेरो अङ्ग्रेजी भाषा सुधार गर्न सक्छु?
+  sentences:
+  - म कसरी एक नेचुरल अंग्रेजी वक्ता बन्न सक्छु?
+  - म जहाँ कुनै मूल अंग्रेजी वक्ताहरू छन् जो मेरो साथ मित्र बन्न चाहन्छन् र मलाई मद्दत
+    गर्न चाहन्छन्?
+  - ने टी २०१ 6 को लागि निजी कलेजहरूको लागि एमबीबीएसको लागि के कटअफ हुनेछ?
+- source_sentence: समय यात्रा सम्भव छ कि छैन? यदि छ भने, कसरी?
+  sentences:
+  - अन्धकारमय वेब सुरक्षित छ कि छैन ब्राउज गर्न?
+  - यदि कुनै बितेको समय राम्रो थियो र समयको यात्रा सम्भव थियो भने म किन वर्तमान समयमा
+    बाँचिरहेको छु?
+  - भविष्यमा समय यात्रा सम्भव हुनेछ कि छैन?
+- source_sentence: म कसरी बिस्तारै तौल घटाउन सक्छु?
+  sentences:
+  - कसरी कुनै केटाले त्यो केटीसँग बदला लिन सक्छ जसले उसलाई धोका दिएको छ?
+  - कस्तो प्रकारको आहार कसैले आहार नचाहने व्यक्तिका लागि उत्तम हुन्छ?
+  - वजन घटाउनको लागि कुनै राम्रो आहार हो?
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the universalml0/nepali_embedding_dataset dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) <!-- at revision baa7be480a7de1539afce709c8f13f833a510e0a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - universalml0/nepali_embedding_dataset
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("universalml0/finetuned_embedding_model_e5-large-multilingual-large")
+# Run inference
+sentences = [
+    'म कसरी बिस्तारै तौल घटाउन सक्छु?',
+    'वजन घटाउनको लागि कुनै राम्रो आहार हो?',
+    'कस्तो प्रकारको आहार कसैले आहार नचाहने व्यक्तिका लागि उत्तम हुन्छ?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### universalml0/nepali_embedding_dataset
+
+* Dataset: universalml0/nepali_embedding_dataset
+* Size: 45,199 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 17.53 tokens</li><li>max: 486 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.68 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.9 tokens</li><li>max: 156 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                  | positive                                                                                                                                             | negative                                                                                                                                                  |
+  |:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>भारतीय सरकारले ५०० र १००० रुपयाको नोटमाथि प्रतिबन्ध लगाउनुको कारण के थियो?</code> | <code>भारतीय सरकारले ५०० र १००० को नोटलाई निष्क्रिय पारेको छ तर तिनीहरूलाई ५०० र २००० को नोटहरूसँग प्रतिस्थापन गरेको छ। के यो विरोधाभासी छैन?</code> | <code>भारतीय सरकारले किन चाहेको भए सीमित मात्रामा नोटहरू मुद्रण गर्न र बजेट घाटा क्लियर गर्न सक्दैन? विशेष गरी, किन कुनै पनि देशले यो गर्न सक्दैन?</code> |
+  | <code>भारतीय हुनुको अनुभूति कस्तो हुन्छ?</code>                                         | <code>भारतीय हुनुको अनुभूति कस्तो हुन्छ?</code>                                                                                                      | <code>भारतीय महिला हुनुको अनुभव कस्तो हुन्छ?</code>                                                                                                       |
+  | <code>के कुनै व्यक्तिले edWisor मार्फत कुनै नौकरी पाएको छ?</code>                       | <code>एडवाइजर वैध छ र के कसैले यस मार्फत कुनै नौकरी पाएको छ?</code>                                                                                  | <code>एलिटमसको माध्यमबाट कसैले काम पाएको छ?</code>                                                                                                        |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 4
+- `learning_rate`: 1e-06
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.3
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 1e-06
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.3
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `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
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0088 | 100   | 0.8671        |
+| 0.0177 | 200   | 0.8234        |
+| 0.0265 | 300   | 0.8223        |
+| 0.0354 | 400   | 0.7423        |
+| 0.0442 | 500   | 0.6605        |
+| 0.0531 | 600   | 0.5558        |
+| 0.0619 | 700   | 0.4076        |
+| 0.0708 | 800   | 0.3617        |
+| 0.0796 | 900   | 0.3087        |
+| 0.0885 | 1000  | 0.2747        |
+| 0.0973 | 1100  | 0.2409        |
+| 0.1062 | 1200  | 0.229         |
+| 0.1150 | 1300  | 0.209         |
+| 0.1239 | 1400  | 0.2556        |
+| 0.1327 | 1500  | 0.2536        |
+| 0.1416 | 1600  | 0.2092        |
+| 0.1504 | 1700  | 0.2464        |
+| 0.1593 | 1800  | 0.1727        |
+| 0.1681 | 1900  | 0.281         |
+| 0.1770 | 2000  | 0.2289        |
+| 0.1858 | 2100  | 0.2065        |
+| 0.1947 | 2200  | 0.1751        |
+| 0.2035 | 2300  | 0.231         |
+| 0.2124 | 2400  | 0.2127        |
+| 0.2212 | 2500  | 0.1908        |
+| 0.2301 | 2600  | 0.2131        |
+| 0.2389 | 2700  | 0.1704        |
+| 0.2478 | 2800  | 0.1923        |
+| 0.2566 | 2900  | 0.1635        |
+| 0.2655 | 3000  | 0.2061        |
+| 0.2743 | 3100  | 0.1843        |
+| 0.2832 | 3200  | 0.1443        |
+| 0.2920 | 3300  | 0.1513        |
+| 0.3009 | 3400  | 0.1879        |
+| 0.3097 | 3500  | 0.2372        |
+| 0.3186 | 3600  | 0.1542        |
+| 0.3274 | 3700  | 0.2523        |
+| 0.3363 | 3800  | 0.2055        |
+| 0.3451 | 3900  | 0.1474        |
+| 0.3540 | 4000  | 0.1647        |
+| 0.3628 | 4100  | 0.1615        |
+| 0.3717 | 4200  | 0.1271        |
+| 0.3805 | 4300  | 0.1451        |
+| 0.3894 | 4400  | 0.1887        |
+| 0.3982 | 4500  | 0.1334        |
+| 0.4071 | 4600  | 0.1962        |
+| 0.4159 | 4700  | 0.1695        |
+| 0.4248 | 4800  | 0.1561        |
+| 0.4336 | 4900  | 0.1146        |
+| 0.4425 | 5000  | 0.1381        |
+| 0.4513 | 5100  | 0.1452        |
+| 0.4602 | 5200  | 0.2388        |
+| 0.4690 | 5300  | 0.1951        |
+| 0.4779 | 5400  | 0.1142        |
+| 0.4867 | 5500  | 0.182         |
+| 0.4956 | 5600  | 0.1968        |
+| 0.5044 | 5700  | 0.1744        |
+| 0.5133 | 5800  | 0.1868        |
+| 0.5221 | 5900  | 0.1452        |
+| 0.5310 | 6000  | 0.1345        |
+| 0.5398 | 6100  | 0.1318        |
+| 0.5487 | 6200  | 0.218         |
+| 0.5575 | 6300  | 0.2118        |
+| 0.5664 | 6400  | 0.1972        |
+| 0.5752 | 6500  | 0.0935        |
+| 0.5841 | 6600  | 0.1991        |
+| 0.5929 | 6700  | 0.1252        |
+| 0.6018 | 6800  | 0.1128        |
+| 0.6106 | 6900  | 0.1585        |
+| 0.6195 | 7000  | 0.2293        |
+| 0.6283 | 7100  | 0.2104        |
+| 0.6372 | 7200  | 0.1416        |
+| 0.6460 | 7300  | 0.2004        |
+| 0.6549 | 7400  | 0.1446        |
+| 0.6637 | 7500  | 0.1171        |
+| 0.6726 | 7600  | 0.1386        |
+| 0.6814 | 7700  | 0.1291        |
+| 0.6903 | 7800  | 0.1546        |
+| 0.6991 | 7900  | 0.1484        |
+| 0.7080 | 8000  | 0.129         |
+| 0.7168 | 8100  | 0.1873        |
+| 0.7257 | 8200  | 0.1333        |
+| 0.7345 | 8300  | 0.1713        |
+| 0.7434 | 8400  | 0.1016        |
+| 0.7522 | 8500  | 0.1519        |
+| 0.7611 | 8600  | 0.1851        |
+| 0.7699 | 8700  | 0.144         |
+| 0.7788 | 8800  | 0.1488        |
+| 0.7876 | 8900  | 0.1568        |
+| 0.7965 | 9000  | 0.1672        |
+| 0.8053 | 9100  | 0.1236        |
+| 0.8142 | 9200  | 0.0973        |
+| 0.8230 | 9300  | 0.1491        |
+| 0.8319 | 9400  | 0.2251        |
+| 0.8407 | 9500  | 0.1433        |
+| 0.8496 | 9600  | 0.2634        |
+| 0.8584 | 9700  | 0.1723        |
+| 0.8673 | 9800  | 0.2373        |
+| 0.8761 | 9900  | 0.1065        |
+| 0.8850 | 10000 | 0.1578        |
+| 0.8938 | 10100 | 0.1127        |
+| 0.9027 | 10200 | 0.1632        |
+| 0.9115 | 10300 | 0.19          |
+| 0.9204 | 10400 | 0.0958        |
+| 0.9292 | 10500 | 0.1029        |
+| 0.9381 | 10600 | 0.1183        |
+| 0.9469 | 10700 | 0.1779        |
+| 0.9558 | 10800 | 0.1571        |
+| 0.9646 | 10900 | 0.1666        |
+| 0.9735 | 11000 | 0.1405        |
+| 0.9823 | 11100 | 0.147         |
+| 0.9912 | 11200 | 0.1428        |
+| 1.0    | 11300 | 0.1724        |
+
+</details>
+
+### Framework Versions
+- Python: 3.9.5
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-large-instruct",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:9896ca3aa5099620001a7eba78c5dbf064c5b531ca15184e4913f40ef1549cd0
+size 2239607176

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
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+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}