Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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_Dense/config.json

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+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ffbcac47ef2d8cec454a28543d8f383326a5d20ee044fa03ce473ead5e9050d5
+size 2362528

README.md

@@ -0,0 +1,860 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:257886
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/LaBSE
+widget:
+- source_sentence: 'Karwa Chauth is a festival celebrated by Hindu women of Northern
+    and Western India on the fourth day after Purnima in the month of Kartika.
+
+    '
+  sentences:
+  - 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा
+    अजायत।
+
+    '
+  - '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति
+    ।"'
+  - 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः
+    अनन्तरं चतुर्थदिने आचर्यते।
+
+    '
+- source_sentence: '"""And if any man will hurt them, fire proceedeth out of their
+    mouth, and devoureth their enemies: and if any man will hurt them, he must in
+    this manner be killed."""'
+  sentences:
+  - '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"'
+  - यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः
+    शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं।
+  - यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं
+    समारभ॥
+- source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the
+    surface.
+
+    '
+  sentences:
+  - उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥
+  - 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते।
+
+    '
+  - आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥
+- source_sentence: 'If you''re planning to fund part or all of your child''s higher
+    education, it''s best to start saving early on.
+
+    '
+  sentences:
+  - समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥
+  - 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम्
+    इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्।
+
+    '
+  - '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ,
+    मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""'
+- source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting
+    off the out-stretched bow of his foe with three shafts, killing his driver with
+    a razor like shaft and making away with his banner with three and his four horses
+    with four other shafts.
+  sentences:
+  - तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च
+    पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥
+  - एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः ।
+  - क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- src2trg_accuracy
+- trg2src_accuracy
+- mean_accuracy
+model-index:
+- name: SentenceTransformer based on sentence-transformers/LaBSE
+  results:
+  - task:
+      type: translation
+      name: Translation
+    dataset:
+      name: eval en sa
+      type: eval-en-sa
+    metrics:
+    - type: src2trg_accuracy
+      value: 0.944
+      name: Src2Trg Accuracy
+    - type: trg2src_accuracy
+      value: 0.947
+      name: Trg2Src Accuracy
+    - type: mean_accuracy
+      value: 0.9455
+      name: Mean Accuracy
+---
+
+# SentenceTransformer based on sentence-transformers/LaBSE
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    "In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.",
+    'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥',
+    'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Translation
+
+* Dataset: `eval-en-sa`
+* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
+
+| Metric            | Value      |
+|:------------------|:-----------|
+| src2trg_accuracy  | 0.944      |
+| trg2src_accuracy  | 0.947      |
+| **mean_accuracy** | **0.9455** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 257,886 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 31.6 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 40.18 tokens</li><li>max: 128 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                        | sentence_1                                                                                                                                                |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>It normally connects to port 80 on a computer.<br></code>                                                                                                                                                                                   | <code>इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति।<br></code>                                                                        |
+  | <code>He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.</code> | <code>सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।</code>                                 |
+  | <code>By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh.<br></code>                                                                                                        | <code>१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्।<br></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
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `num_train_epochs`: 15
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 15
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `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
+- `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`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step   | Training Loss | eval-en-sa_mean_accuracy |
+|:-------:|:------:|:-------------:|:------------------------:|
+| 0.0310  | 500    | 0.4289        | -                        |
+| 0.0620  | 1000   | 0.182         | -                        |
+| 0.0931  | 1500   | 0.1405        | -                        |
+| 0.1241  | 2000   | 0.1097        | -                        |
+| 0.1551  | 2500   | 0.0911        | -                        |
+| 0.1861  | 3000   | 0.0791        | -                        |
+| 0.2171  | 3500   | 0.0725        | -                        |
+| 0.2482  | 4000   | 0.067         | -                        |
+| 0.2792  | 4500   | 0.0594        | -                        |
+| 0.3102  | 5000   | 0.0629        | -                        |
+| 0.3412  | 5500   | 0.0535        | -                        |
+| 0.3723  | 6000   | 0.0512        | -                        |
+| 0.4033  | 6500   | 0.0456        | -                        |
+| 0.4343  | 7000   | 0.0462        | -                        |
+| 0.4653  | 7500   | 0.043         | -                        |
+| 0.4963  | 8000   | 0.0425        | -                        |
+| 0.5274  | 8500   | 0.0412        | -                        |
+| 0.5584  | 9000   | 0.0418        | -                        |
+| 0.5894  | 9500   | 0.0415        | -                        |
+| 0.6204  | 10000  | 0.0409        | -                        |
+| 0.6514  | 10500  | 0.04          | -                        |
+| 0.6825  | 11000  | 0.032         | -                        |
+| 0.7135  | 11500  | 0.0323        | -                        |
+| 0.7445  | 12000  | 0.0325        | -                        |
+| 0.7755  | 12500  | 0.0355        | -                        |
+| 0.8066  | 13000  | 0.0285        | -                        |
+| 0.8376  | 13500  | 0.0281        | -                        |
+| 0.8686  | 14000  | 0.0289        | -                        |
+| 0.8996  | 14500  | 0.033         | -                        |
+| 0.9306  | 15000  | 0.0336        | -                        |
+| 0.9617  | 15500  | 0.0335        | -                        |
+| 0.9927  | 16000  | 0.0278        | -                        |
+| 1.0     | 16118  | -             | 0.913                    |
+| 1.0237  | 16500  | 0.0312        | -                        |
+| 1.0547  | 17000  | 0.0294        | -                        |
+| 1.0857  | 17500  | 0.0288        | -                        |
+| 1.1168  | 18000  | 0.0287        | -                        |
+| 1.1478  | 18500  | 0.0245        | -                        |
+| 1.1788  | 19000  | 0.0243        | -                        |
+| 1.2098  | 19500  | 0.022         | -                        |
+| 1.2408  | 20000  | 0.0266        | -                        |
+| 1.2719  | 20500  | 0.0224        | -                        |
+| 1.3029  | 21000  | 0.0283        | -                        |
+| 1.3339  | 21500  | 0.02          | -                        |
+| 1.3649  | 22000  | 0.0212        | -                        |
+| 1.3960  | 22500  | 0.0197        | -                        |
+| 1.4270  | 23000  | 0.0174        | -                        |
+| 1.4580  | 23500  | 0.0179        | -                        |
+| 1.4890  | 24000  | 0.0187        | -                        |
+| 1.5200  | 24500  | 0.0191        | -                        |
+| 1.5511  | 25000  | 0.0151        | -                        |
+| 1.5821  | 25500  | 0.0161        | -                        |
+| 1.6131  | 26000  | 0.0182        | -                        |
+| 1.6441  | 26500  | 0.0155        | -                        |
+| 1.6751  | 27000  | 0.013         | -                        |
+| 1.7062  | 27500  | 0.0119        | -                        |
+| 1.7372  | 28000  | 0.0119        | -                        |
+| 1.7682  | 28500  | 0.0133        | -                        |
+| 1.7992  | 29000  | 0.0113        | -                        |
+| 1.8303  | 29500  | 0.011         | -                        |
+| 1.8613  | 30000  | 0.0133        | -                        |
+| 1.8923  | 30500  | 0.0114        | -                        |
+| 1.9233  | 31000  | 0.0139        | -                        |
+| 1.9543  | 31500  | 0.0131        | -                        |
+| 1.9854  | 32000  | 0.0115        | -                        |
+| 2.0     | 32236  | -             | 0.9345                   |
+| 2.0164  | 32500  | 0.01          | -                        |
+| 2.0474  | 33000  | 0.01          | -                        |
+| 2.0784  | 33500  | 0.0091        | -                        |
+| 2.1094  | 34000  | 0.0131        | -                        |
+| 2.1405  | 34500  | 0.0096        | -                        |
+| 2.1715  | 35000  | 0.0095        | -                        |
+| 2.2025  | 35500  | 0.0103        | -                        |
+| 2.2335  | 36000  | 0.0101        | -                        |
+| 2.2645  | 36500  | 0.0102        | -                        |
+| 2.2956  | 37000  | 0.0102        | -                        |
+| 2.3266  | 37500  | 0.0085        | -                        |
+| 2.3576  | 38000  | 0.0087        | -                        |
+| 2.3886  | 38500  | 0.0103        | -                        |
+| 2.4197  | 39000  | 0.0058        | -                        |
+| 2.4507  | 39500  | 0.0086        | -                        |
+| 2.4817  | 40000  | 0.0088        | -                        |
+| 2.5127  | 40500  | 0.0088        | -                        |
+| 2.5437  | 41000  | 0.007         | -                        |
+| 2.5748  | 41500  | 0.0082        | -                        |
+| 2.6058  | 42000  | 0.0069        | -                        |
+| 2.6368  | 42500  | 0.0071        | -                        |
+| 2.6678  | 43000  | 0.0058        | -                        |
+| 2.6988  | 43500  | 0.0075        | -                        |
+| 2.7299  | 44000  | 0.0064        | -                        |
+| 2.7609  | 44500  | 0.0053        | -                        |
+| 2.7919  | 45000  | 0.0055        | -                        |
+| 2.8229  | 45500  | 0.0061        | -                        |
+| 2.8540  | 46000  | 0.0059        | -                        |
+| 2.8850  | 46500  | 0.0062        | -                        |
+| 2.9160  | 47000  | 0.0046        | -                        |
+| 2.9470  | 47500  | 0.0064        | -                        |
+| 2.9780  | 48000  | 0.0053        | -                        |
+| 3.0     | 48354  | -             | 0.941                    |
+| 3.0091  | 48500  | 0.0048        | -                        |
+| 3.0401  | 49000  | 0.0059        | -                        |
+| 3.0711  | 49500  | 0.005         | -                        |
+| 3.1021  | 50000  | 0.005         | 0.9415                   |
+| 3.1331  | 50500  | 0.0046        | -                        |
+| 3.1642  | 51000  | 0.005         | -                        |
+| 3.1952  | 51500  | 0.0051        | -                        |
+| 3.2262  | 52000  | 0.0041        | -                        |
+| 3.2572  | 52500  | 0.0052        | -                        |
+| 3.2882  | 53000  | 0.0052        | -                        |
+| 3.3193  | 53500  | 0.0053        | -                        |
+| 3.3503  | 54000  | 0.0041        | -                        |
+| 3.3813  | 54500  | 0.0042        | -                        |
+| 3.4123  | 55000  | 0.0026        | -                        |
+| 3.4434  | 55500  | 0.0045        | -                        |
+| 3.4744  | 56000  | 0.0045        | -                        |
+| 3.5054  | 56500  | 0.0054        | -                        |
+| 3.5364  | 57000  | 0.0055        | -                        |
+| 3.5674  | 57500  | 0.0046        | -                        |
+| 3.5985  | 58000  | 0.0045        | -                        |
+| 3.6295  | 58500  | 0.0041        | -                        |
+| 3.6605  | 59000  | 0.0037        | -                        |
+| 3.6915  | 59500  | 0.003         | -                        |
+| 3.7225  | 60000  | 0.0039        | -                        |
+| 3.7536  | 60500  | 0.0027        | -                        |
+| 3.7846  | 61000  | 0.0041        | -                        |
+| 3.8156  | 61500  | 0.003         | -                        |
+| 3.8466  | 62000  | 0.0027        | -                        |
+| 3.8777  | 62500  | 0.0039        | -                        |
+| 3.9087  | 63000  | 0.0038        | -                        |
+| 3.9397  | 63500  | 0.0029        | -                        |
+| 3.9707  | 64000  | 0.0037        | -                        |
+| 4.0     | 64472  | -             | 0.9365                   |
+| 4.0017  | 64500  | 0.0023        | -                        |
+| 4.0328  | 65000  | 0.0034        | -                        |
+| 4.0638  | 65500  | 0.0033        | -                        |
+| 4.0948  | 66000  | 0.0033        | -                        |
+| 4.1258  | 66500  | 0.004         | -                        |
+| 4.1568  | 67000  | 0.0026        | -                        |
+| 4.1879  | 67500  | 0.0026        | -                        |
+| 4.2189  | 68000  | 0.0025        | -                        |
+| 4.2499  | 68500  | 0.0037        | -                        |
+| 4.2809  | 69000  | 0.0041        | -                        |
+| 4.3119  | 69500  | 0.0031        | -                        |
+| 4.3430  | 70000  | 0.0025        | -                        |
+| 4.3740  | 70500  | 0.0025        | -                        |
+| 4.4050  | 71000  | 0.0022        | -                        |
+| 4.4360  | 71500  | 0.0016        | -                        |
+| 4.4671  | 72000  | 0.003         | -                        |
+| 4.4981  | 72500  | 0.0029        | -                        |
+| 4.5291  | 73000  | 0.003         | -                        |
+| 4.5601  | 73500  | 0.0025        | -                        |
+| 4.5911  | 74000  | 0.0027        | -                        |
+| 4.6222  | 74500  | 0.0028        | -                        |
+| 4.6532  | 75000  | 0.003         | -                        |
+| 4.6842  | 75500  | 0.002         | -                        |
+| 4.7152  | 76000  | 0.0028        | -                        |
+| 4.7462  | 76500  | 0.0016        | -                        |
+| 4.7773  | 77000  | 0.0022        | -                        |
+| 4.8083  | 77500  | 0.0019        | -                        |
+| 4.8393  | 78000  | 0.0019        | -                        |
+| 4.8703  | 78500  | 0.0026        | -                        |
+| 4.9014  | 79000  | 0.0023        | -                        |
+| 4.9324  | 79500  | 0.0016        | -                        |
+| 4.9634  | 80000  | 0.0019        | -                        |
+| 4.9944  | 80500  | 0.0018        | -                        |
+| 5.0     | 80590  | -             | 0.937                    |
+| 5.0254  | 81000  | 0.0028        | -                        |
+| 5.0565  | 81500  | 0.0019        | -                        |
+| 5.0875  | 82000  | 0.0024        | -                        |
+| 5.1185  | 82500  | 0.0016        | -                        |
+| 5.1495  | 83000  | 0.0015        | -                        |
+| 5.1805  | 83500  | 0.0017        | -                        |
+| 5.2116  | 84000  | 0.0016        | -                        |
+| 5.2426  | 84500  | 0.0026        | -                        |
+| 5.2736  | 85000  | 0.0029        | -                        |
+| 5.3046  | 85500  | 0.0027        | -                        |
+| 5.3356  | 86000  | 0.002         | -                        |
+| 5.3667  | 86500  | 0.002         | -                        |
+| 5.3977  | 87000  | 0.0021        | -                        |
+| 5.4287  | 87500  | 0.0011        | -                        |
+| 5.4597  | 88000  | 0.0016        | -                        |
+| 5.4908  | 88500  | 0.0019        | -                        |
+| 5.5218  | 89000  | 0.0027        | -                        |
+| 5.5528  | 89500  | 0.0012        | -                        |
+| 5.5838  | 90000  | 0.0012        | -                        |
+| 5.6148  | 90500  | 0.0016        | -                        |
+| 5.6459  | 91000  | 0.0019        | -                        |
+| 5.6769  | 91500  | 0.0016        | -                        |
+| 5.7079  | 92000  | 0.0027        | -                        |
+| 5.7389  | 92500  | 0.0013        | -                        |
+| 5.7699  | 93000  | 0.0013        | -                        |
+| 5.8010  | 93500  | 0.0015        | -                        |
+| 5.8320  | 94000  | 0.0016        | -                        |
+| 5.8630  | 94500  | 0.002         | -                        |
+| 5.8940  | 95000  | 0.001         | -                        |
+| 5.9251  | 95500  | 0.0014        | -                        |
+| 5.9561  | 96000  | 0.0021        | -                        |
+| 5.9871  | 96500  | 0.0022        | -                        |
+| 6.0     | 96708  | -             | 0.933                    |
+| 6.0181  | 97000  | 0.0016        | -                        |
+| 6.0491  | 97500  | 0.0015        | -                        |
+| 6.0802  | 98000  | 0.0011        | -                        |
+| 6.1112  | 98500  | 0.0016        | -                        |
+| 6.1422  | 99000  | 0.001         | -                        |
+| 6.1732  | 99500  | 0.0013        | -                        |
+| 6.2042  | 100000 | 0.0015        | 0.9365                   |
+| 6.2353  | 100500 | 0.0017        | -                        |
+| 6.2663  | 101000 | 0.0015        | -                        |
+| 6.2973  | 101500 | 0.0016        | -                        |
+| 6.3283  | 102000 | 0.001         | -                        |
+| 6.3593  | 102500 | 0.0013        | -                        |
+| 6.3904  | 103000 | 0.0013        | -                        |
+| 6.4214  | 103500 | 0.0011        | -                        |
+| 6.4524  | 104000 | 0.0007        | -                        |
+| 6.4834  | 104500 | 0.0013        | -                        |
+| 6.5145  | 105000 | 0.0011        | -                        |
+| 6.5455  | 105500 | 0.0011        | -                        |
+| 6.5765  | 106000 | 0.0015        | -                        |
+| 6.6075  | 106500 | 0.002         | -                        |
+| 6.6385  | 107000 | 0.0011        | -                        |
+| 6.6696  | 107500 | 0.0013        | -                        |
+| 6.7006  | 108000 | 0.0017        | -                        |
+| 6.7316  | 108500 | 0.0008        | -                        |
+| 6.7626  | 109000 | 0.0011        | -                        |
+| 6.7936  | 109500 | 0.0008        | -                        |
+| 6.8247  | 110000 | 0.0009        | -                        |
+| 6.8557  | 110500 | 0.0014        | -                        |
+| 6.8867  | 111000 | 0.0014        | -                        |
+| 6.9177  | 111500 | 0.0014        | -                        |
+| 6.9488  | 112000 | 0.0014        | -                        |
+| 6.9798  | 112500 | 0.0013        | -                        |
+| 7.0     | 112826 | -             | 0.9390                   |
+| 7.0108  | 113000 | 0.0011        | -                        |
+| 7.0418  | 113500 | 0.0013        | -                        |
+| 7.0728  | 114000 | 0.0012        | -                        |
+| 7.1039  | 114500 | 0.001         | -                        |
+| 7.1349  | 115000 | 0.0016        | -                        |
+| 7.1659  | 115500 | 0.0009        | -                        |
+| 7.1969  | 116000 | 0.0009        | -                        |
+| 7.2279  | 116500 | 0.0007        | -                        |
+| 7.2590  | 117000 | 0.0008        | -                        |
+| 7.2900  | 117500 | 0.0014        | -                        |
+| 7.3210  | 118000 | 0.0012        | -                        |
+| 7.3520  | 118500 | 0.0007        | -                        |
+| 7.3831  | 119000 | 0.001         | -                        |
+| 7.4141  | 119500 | 0.001         | -                        |
+| 7.4451  | 120000 | 0.0007        | -                        |
+| 7.4761  | 120500 | 0.0008        | -                        |
+| 7.5071  | 121000 | 0.0009        | -                        |
+| 7.5382  | 121500 | 0.0009        | -                        |
+| 7.5692  | 122000 | 0.001         | -                        |
+| 7.6002  | 122500 | 0.0009        | -                        |
+| 7.6312  | 123000 | 0.0007        | -                        |
+| 7.6622  | 123500 | 0.0009        | -                        |
+| 7.6933  | 124000 | 0.0007        | -                        |
+| 7.7243  | 124500 | 0.0012        | -                        |
+| 7.7553  | 125000 | 0.001         | -                        |
+| 7.7863  | 125500 | 0.0005        | -                        |
+| 7.8173  | 126000 | 0.0005        | -                        |
+| 7.8484  | 126500 | 0.0008        | -                        |
+| 7.8794  | 127000 | 0.0014        | -                        |
+| 7.9104  | 127500 | 0.0014        | -                        |
+| 7.9414  | 128000 | 0.0009        | -                        |
+| 7.9725  | 128500 | 0.0008        | -                        |
+| 8.0     | 128944 | -             | 0.94                     |
+| 8.0035  | 129000 | 0.0013        | -                        |
+| 8.0345  | 129500 | 0.0007        | -                        |
+| 8.0655  | 130000 | 0.0007        | -                        |
+| 8.0965  | 130500 | 0.0008        | -                        |
+| 8.1276  | 131000 | 0.0009        | -                        |
+| 8.1586  | 131500 | 0.0009        | -                        |
+| 8.1896  | 132000 | 0.0007        | -                        |
+| 8.2206  | 132500 | 0.0008        | -                        |
+| 8.2516  | 133000 | 0.0008        | -                        |
+| 8.2827  | 133500 | 0.0006        | -                        |
+| 8.3137  | 134000 | 0.0008        | -                        |
+| 8.3447  | 134500 | 0.001         | -                        |
+| 8.3757  | 135000 | 0.0006        | -                        |
+| 8.4068  | 135500 | 0.0007        | -                        |
+| 8.4378  | 136000 | 0.0007        | -                        |
+| 8.4688  | 136500 | 0.0009        | -                        |
+| 8.4998  | 137000 | 0.0008        | -                        |
+| 8.5308  | 137500 | 0.0006        | -                        |
+| 8.5619  | 138000 | 0.0008        | -                        |
+| 8.5929  | 138500 | 0.0007        | -                        |
+| 8.6239  | 139000 | 0.0008        | -                        |
+| 8.6549  | 139500 | 0.0006        | -                        |
+| 8.6859  | 140000 | 0.0005        | -                        |
+| 8.7170  | 140500 | 0.0006        | -                        |
+| 8.7480  | 141000 | 0.0006        | -                        |
+| 8.7790  | 141500 | 0.0006        | -                        |
+| 8.8100  | 142000 | 0.0005        | -                        |
+| 8.8410  | 142500 | 0.0006        | -                        |
+| 8.8721  | 143000 | 0.0005        | -                        |
+| 8.9031  | 143500 | 0.0006        | -                        |
+| 8.9341  | 144000 | 0.0009        | -                        |
+| 8.9651  | 144500 | 0.0007        | -                        |
+| 8.9962  | 145000 | 0.0007        | -                        |
+| 9.0     | 145062 | -             | 0.938                    |
+| 9.0272  | 145500 | 0.0007        | -                        |
+| 9.0582  | 146000 | 0.0007        | -                        |
+| 9.0892  | 146500 | 0.0007        | -                        |
+| 9.1202  | 147000 | 0.0007        | -                        |
+| 9.1513  | 147500 | 0.0005        | -                        |
+| 9.1823  | 148000 | 0.0005        | -                        |
+| 9.2133  | 148500 | 0.0005        | -                        |
+| 9.2443  | 149000 | 0.0007        | -                        |
+| 9.2753  | 149500 | 0.0006        | -                        |
+| 9.3064  | 150000 | 0.0005        | 0.938                    |
+| 9.3374  | 150500 | 0.0005        | -                        |
+| 9.3684  | 151000 | 0.0004        | -                        |
+| 9.3994  | 151500 | 0.0007        | -                        |
+| 9.4305  | 152000 | 0.0006        | -                        |
+| 9.4615  | 152500 | 0.0006        | -                        |
+| 9.4925  | 153000 | 0.0012        | -                        |
+| 9.5235  | 153500 | 0.0015        | -                        |
+| 9.5545  | 154000 | 0.0006        | -                        |
+| 9.5856  | 154500 | 0.0004        | -                        |
+| 9.6166  | 155000 | 0.0004        | -                        |
+| 9.6476  | 155500 | 0.0007        | -                        |
+| 9.6786  | 156000 | 0.0005        | -                        |
+| 9.7096  | 156500 | 0.0006        | -                        |
+| 9.7407  | 157000 | 0.0004        | -                        |
+| 9.7717  | 157500 | 0.0004        | -                        |
+| 9.8027  | 158000 | 0.0006        | -                        |
+| 9.8337  | 158500 | 0.0004        | -                        |
+| 9.8647  | 159000 | 0.0005        | -                        |
+| 9.8958  | 159500 | 0.0005        | -                        |
+| 9.9268  | 160000 | 0.0004        | -                        |
+| 9.9578  | 160500 | 0.0007        | -                        |
+| 9.9888  | 161000 | 0.0008        | -                        |
+| 10.0    | 161180 | -             | 0.9405                   |
+| 10.0199 | 161500 | 0.0009        | -                        |
+| 10.0509 | 162000 | 0.0007        | -                        |
+| 10.0819 | 162500 | 0.0007        | -                        |
+| 10.1129 | 163000 | 0.0007        | -                        |
+| 10.1439 | 163500 | 0.0005        | -                        |
+| 10.1750 | 164000 | 0.0005        | -                        |
+| 10.2060 | 164500 | 0.0004        | -                        |
+| 10.2370 | 165000 | 0.0006        | -                        |
+| 10.2680 | 165500 | 0.0006        | -                        |
+| 10.2990 | 166000 | 0.0005        | -                        |
+| 10.3301 | 166500 | 0.0005        | -                        |
+| 10.3611 | 167000 | 0.0006        | -                        |
+| 10.3921 | 167500 | 0.0006        | -                        |
+| 10.4231 | 168000 | 0.0003        | -                        |
+| 10.4542 | 168500 | 0.0005        | -                        |
+| 10.4852 | 169000 | 0.001         | -                        |
+| 10.5162 | 169500 | 0.0007        | -                        |
+| 10.5472 | 170000 | 0.0003        | -                        |
+| 10.5782 | 170500 | 0.0005        | -                        |
+| 10.6093 | 171000 | 0.0003        | -                        |
+| 10.6403 | 171500 | 0.0004        | -                        |
+| 10.6713 | 172000 | 0.0006        | -                        |
+| 10.7023 | 172500 | 0.0006        | -                        |
+| 10.7333 | 173000 | 0.0005        | -                        |
+| 10.7644 | 173500 | 0.0004        | -                        |
+| 10.7954 | 174000 | 0.0003        | -                        |
+| 10.8264 | 174500 | 0.0007        | -                        |
+| 10.8574 | 175000 | 0.0005        | -                        |
+| 10.8884 | 175500 | 0.0003        | -                        |
+| 10.9195 | 176000 | 0.0006        | -                        |
+| 10.9505 | 176500 | 0.001         | -                        |
+| 10.9815 | 177000 | 0.0007        | -                        |
+| 11.0    | 177298 | -             | 0.9345                   |
+| 11.0125 | 177500 | 0.0003        | -                        |
+| 11.0436 | 178000 | 0.0003        | -                        |
+| 11.0746 | 178500 | 0.0005        | -                        |
+| 11.1056 | 179000 | 0.0005        | -                        |
+| 11.1366 | 179500 | 0.0007        | -                        |
+| 11.1676 | 180000 | 0.0008        | -                        |
+| 11.1987 | 180500 | 0.0004        | -                        |
+| 11.2297 | 181000 | 0.0006        | -                        |
+| 11.2607 | 181500 | 0.0006        | -                        |
+| 11.2917 | 182000 | 0.0009        | -                        |
+| 11.3227 | 182500 | 0.0005        | -                        |
+| 11.3538 | 183000 | 0.0004        | -                        |
+| 11.3848 | 183500 | 0.0004        | -                        |
+| 11.4158 | 184000 | 0.0005        | -                        |
+| 11.4468 | 184500 | 0.0003        | -                        |
+| 11.4779 | 185000 | 0.0002        | -                        |
+| 11.5089 | 185500 | 0.0003        | -                        |
+| 11.5399 | 186000 | 0.0007        | -                        |
+| 11.5709 | 186500 | 0.0003        | -                        |
+| 11.6019 | 187000 | 0.0003        | -                        |
+| 11.6330 | 187500 | 0.0004        | -                        |
+| 11.6640 | 188000 | 0.0007        | -                        |
+| 11.6950 | 188500 | 0.0003        | -                        |
+| 11.7260 | 189000 | 0.0003        | -                        |
+| 11.7570 | 189500 | 0.0004        | -                        |
+| 11.7881 | 190000 | 0.0004        | -                        |
+| 11.8191 | 190500 | 0.0003        | -                        |
+| 11.8501 | 191000 | 0.0003        | -                        |
+| 11.8811 | 191500 | 0.0003        | -                        |
+| 11.9121 | 192000 | 0.0002        | -                        |
+| 11.9432 | 192500 | 0.0008        | -                        |
+| 11.9742 | 193000 | 0.0004        | -                        |
+| 12.0    | 193416 | -             | 0.944                    |
+| 12.0052 | 193500 | 0.0005        | -                        |
+| 12.0362 | 194000 | 0.0002        | -                        |
+| 12.0673 | 194500 | 0.0003        | -                        |
+| 12.0983 | 195000 | 0.0004        | -                        |
+| 12.1293 | 195500 | 0.0005        | -                        |
+| 12.1603 | 196000 | 0.0004        | -                        |
+| 12.1913 | 196500 | 0.0002        | -                        |
+| 12.2224 | 197000 | 0.0002        | -                        |
+| 12.2534 | 197500 | 0.0003        | -                        |
+| 12.2844 | 198000 | 0.0003        | -                        |
+| 12.3154 | 198500 | 0.0005        | -                        |
+| 12.3464 | 199000 | 0.0004        | -                        |
+| 12.3775 | 199500 | 0.0004        | -                        |
+| 12.4085 | 200000 | 0.0003        | 0.9435                   |
+| 12.4395 | 200500 | 0.0003        | -                        |
+| 12.4705 | 201000 | 0.0004        | -                        |
+| 12.5016 | 201500 | 0.0009        | -                        |
+| 12.5326 | 202000 | 0.0005        | -                        |
+| 12.5636 | 202500 | 0.0003        | -                        |
+| 12.5946 | 203000 | 0.0003        | -                        |
+| 12.6256 | 203500 | 0.0002        | -                        |
+| 12.6567 | 204000 | 0.0003        | -                        |
+| 12.6877 | 204500 | 0.0002        | -                        |
+| 12.7187 | 205000 | 0.0005        | -                        |
+| 12.7497 | 205500 | 0.0003        | -                        |
+| 12.7807 | 206000 | 0.0004        | -                        |
+| 12.8118 | 206500 | 0.0003        | -                        |
+| 12.8428 | 207000 | 0.0003        | -                        |
+| 12.8738 | 207500 | 0.0003        | -                        |
+| 12.9048 | 208000 | 0.0003        | -                        |
+| 12.9358 | 208500 | 0.0006        | -                        |
+| 12.9669 | 209000 | 0.0004        | -                        |
+| 12.9979 | 209500 | 0.0004        | -                        |
+| 13.0    | 209534 | -             | 0.9455                   |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.17
+- Sentence Transformers: 4.1.0
+- Transformers: 4.46.3
+- PyTorch: 2.2.0+cu121
+- Accelerate: 1.1.1
+- Datasets: 2.18.0
+- 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",
+}
+```
+
+#### 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

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+{
+  "_name_or_path": "sentence-transformers/LaBSE",
+  "architectures": [
+    "BertModel"
+  ],
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+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 501153
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.46.3",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/translation_evaluation_eval-en-sa_results.csv

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+epoch,steps,src2trg,trg2src
+1.0,16118,0.914,0.912
+2.0,32236,0.942,0.927
+3.0,48354,0.944,0.938
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+14.0,225652,0.947,0.938
+15.0,241770,0.942,0.943

model.safetensors

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+size 1883730160

modules.json

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+[
+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+  {
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+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
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+}

tokenizer.json

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+oid sha256:edba4e57ec22a2a74bbdb601d3f908e4699c34f8386d52ed055e6fe6bd2b51ac
+size 13632172

tokenizer_config.json

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+{
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+  "unk_token": "[UNK]"
+}