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

@@ -0,0 +1,10 @@
+{
+    "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

@@ -0,0 +1,524 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:257886
+- loss:MultipleNegativesRankingLoss
+base_model: intfloat/multilingual-e5-large
+widget:
+- source_sentence: Wherever and whenever they saw any creature, any dweller of the
+    Khandava, escaping from the fire, those two great heroes immediately shot it down.
+  sentences:
+  - वयं पठाम ।
+  - 'दि अमोङ्ग- अस् कुक्कुटस्य खण्डः पैरेडोलिया इत्यस्य उदाहरणम् अस्ति।
+
+    '
+  - यत्र यत्र च दृश्यन्ते प्राणिनः खाण्डवालयाः। पलायन्तः प्रवीरौ तौ तत्र तत्राभ्यधावताम्॥
+- source_sentence: 'Residents were trapped in houses and elsewhere as the roads turned
+    into rivers.
+
+    '
+  sentences:
+  - वयमधुना षट्-लेबल्स् योजितवन्तः।
+  - 'पदवीषु नद्यायमानासु अन्यत्र गन्तुम् अकल्पाः वस्तव्याः गृहेष्वेव निबद्धाः आसन्।
+
+    '
+  - 'स्व॒स्ति न॒ इन्द्रो॑ वृ॒द्धश्र॑वाः स्व॒स्ति नः॑ पू॒षा वि॒श्ववे॑दाः । स्व॒स्ति
+    न॒स्तार्क्ष्यो॒ अरि॑ष्टनेमिः स्व॒स्ति नो॒ बृह॒स्पति॑र्दधातु  '
+- source_sentence: From this street the village is seen.
+  sentences:
+  - धर्मदण्डो न निर्दण्डो धर्मकार्यानुशासकः। यन्त्रितः कार्यकरणैः षड्भागकृतलक्षणः॥
+  - एतस्याः वीथ्याः ग्रामं दृश्यते ।
+  - 'भवता पत्रकर्त्रा नगरे सामुदायिकायाः हिंसायाः विषये मिथ्यावार्ताः प्रकाशिताः इत्यतः
+    जनाः भीताः सन्ति।
+
+    '
+- source_sentence: 'Visitors have put poppies next to the names of their relatives
+    and friends.
+
+    '
+  sentences:
+  - 'परी॒तो षि॑ञ्चता सु॒तं सोमो॒ य उ॑त्त॒मं ह॒विः । द॒ध॒न्वाँ यो नर्यो॑ अ॒प्स्व१॒॑न्तरा
+    सु॒षाव॒ सोम॒मद्रि॑भिः  '
+  - 'सन्दर्शकाः स्वीयानां सम्बन्धिनां, सुहृदां च नाम्नः पार्श्वे पोप्पीस् न्यक्षिपन्।
+
+    '
+  - 'बीबीगढ्-गृहं यत्र आङ्ग्लस्त्रियः, बालकाः च हताः, तथा च कूपः यस्मात् मृतानां शवाः
+    च प्राप्ताः।
+
+    '
+- source_sentence: 'The majority of these nations are now republics or part of republics.
+
+    '
+  sentences:
+  - 'एतेषु अधिकांशाः देशाः अधुना गणराज्यानि उत गणराज्यानां भागाः वा सन्ति।
+
+    '
+  - तदिन्द्रजालप्रतिम बाणजालममित्रहा। विसृज्य दिक्षु सर्वासु महेन्द्र इव वज्रभृत्॥
+  - अत्र मूलसञ्चिका (source file) विद्यते। pdflatex इत्यादेशमुपयुज्य सङ्कलयामि।
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- src2trg_accuracy
+- trg2src_accuracy
+- mean_accuracy
+model-index:
+- name: SentenceTransformer based on intfloat/multilingual-e5-large
+  results:
+  - task:
+      type: translation
+      name: Translation
+    dataset:
+      name: eval en sa
+      type: eval-en-sa
+    metrics:
+    - type: src2trg_accuracy
+      value: 0.866
+      name: Src2Trg Accuracy
+    - type: trg2src_accuracy
+      value: 0.868
+      name: Trg2Src Accuracy
+    - type: mean_accuracy
+      value: 0.867
+      name: Mean Accuracy
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-large
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). 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](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision 0dc5580a448e4284468b8909bae50fa925907bc5 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False, 'architecture': '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'The majority of these nations are now republics or part of republics.\n',
+    'एतेषु अधिकांशाः देशाः अधुना गणराज्यानि उत गणराज्यानां भागाः वा सन्ति।\n',
+    'अत्र मूलसञ्चिका (source file) विद्यते। pdflatex इत्यादेशमुपयुज्य सङ्कलयामि।',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8049, 0.1296],
+#         [0.8049, 1.0000, 0.1642],
+#         [0.1296, 0.1642, 1.0000]])
+```
+
+<!--
+### 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  | 0.866     |
+| trg2src_accuracy  | 0.868     |
+| **mean_accuracy** | **0.867** |
+
+<!--
+## 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: 5 tokens</li><li>mean: 33.91 tokens</li><li>max: 403 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 37.33 tokens</li><li>max: 228 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                  | sentence_1                                                                                                                                                                    |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>"For the purpose of this tutorial, we shall list these  instructions in slides."</code>                                                                                                                                               | <code>अस्य पाठस्य आनुकूल्याय स्लैड् द्वारा आदेशान् वदामः ।</code>                                                                                                             |
+  | <code>Gandharva prajapati, Vishwakarma and mana swaroop. Please protect Gandharva Brahmins and Kshatriyas. Riku and Sama have an apsara named Ashti. Please protect us. This sacrifice is an offering for them. Swaha for them. (43)</code> | <code>प्र॒जाप॑तिर्वि॒श्वक॑र्मा॒ मनो॑ गन्ध॒र्वस्तस्य॑ऽऋ॒क्सा॒मान्य॑प्स॒रस॒ऽएष्ट॑यो॒ नाम॑। स न॑ऽइ॒दं ब्रह्म॑ क्ष॒त्रं पा॑तु॒ तस्मै॒ स्वाहा॒ वाट् ताभ्यः॒ स्वाहा॑ ॥  (४३)</code> |
+  | <code>Many things are sold to treat acne, the most popular being benzoyl peroxide.<br></code>                                                                                                                                               | <code>आक्ने-चिकित्सार्थं नाइकानि वस्तूनि विक्रीयन्ते, तेषु अतिजनप्रियं बेन्ज़ोय्ल् पराक्सैड्।<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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `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
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | eval-en-sa_mean_accuracy |
+|:------:|:-----:|:-------------:|:------------------------:|
+| 0.0078 | 500   | 0.2715        | -                        |
+| 0.0155 | 1000  | 0.0402        | -                        |
+| 0.0233 | 1500  | 0.0323        | -                        |
+| 0.0310 | 2000  | 0.0305        | -                        |
+| 0.0388 | 2500  | 0.0169        | -                        |
+| 0.0465 | 3000  | 0.0122        | -                        |
+| 0.0543 | 3500  | 0.011         | -                        |
+| 0.0620 | 4000  | 0.0134        | -                        |
+| 0.0698 | 4500  | 0.0081        | -                        |
+| 0.0776 | 5000  | 0.0177        | -                        |
+| 0.0853 | 5500  | 0.0195        | -                        |
+| 0.0931 | 6000  | 0.014         | -                        |
+| 0.1008 | 6500  | 0.0226        | -                        |
+| 0.1086 | 7000  | 0.0122        | -                        |
+| 0.1163 | 7500  | 0.0156        | -                        |
+| 0.1241 | 8000  | 0.0192        | -                        |
+| 0.1318 | 8500  | 0.023         | -                        |
+| 0.1396 | 9000  | 0.0153        | -                        |
+| 0.1474 | 9500  | 0.0275        | -                        |
+| 0.1551 | 10000 | 0.0272        | -                        |
+| 0.1629 | 10500 | 0.0222        | -                        |
+| 0.1706 | 11000 | 0.0134        | -                        |
+| 0.1784 | 11500 | 0.0216        | -                        |
+| 0.1861 | 12000 | 0.0152        | -                        |
+| 0.1939 | 12500 | 0.0104        | -                        |
+| 0.2016 | 13000 | 0.0178        | -                        |
+| 0.2094 | 13500 | 0.0209        | -                        |
+| 0.2171 | 14000 | 0.0211        | -                        |
+| 0.2249 | 14500 | 0.0198        | -                        |
+| 0.2327 | 15000 | 0.0212        | -                        |
+| 0.2404 | 15500 | 0.0177        | -                        |
+| 0.2482 | 16000 | 0.0221        | -                        |
+| 0.2559 | 16500 | 0.0206        | -                        |
+| 0.2637 | 17000 | 0.0181        | -                        |
+| 0.2714 | 17500 | 0.0165        | -                        |
+| 0.2792 | 18000 | 0.0145        | -                        |
+| 0.2869 | 18500 | 0.0139        | -                        |
+| 0.2947 | 19000 | 0.0198        | -                        |
+| 0.3025 | 19500 | 0.0139        | -                        |
+| 0.3102 | 20000 | 0.0177        | -                        |
+| 0.3180 | 20500 | 0.0104        | -                        |
+| 0.3257 | 21000 | 0.0149        | -                        |
+| 0.3335 | 21500 | 0.0144        | -                        |
+| 0.3412 | 22000 | 0.0168        | -                        |
+| 0.3490 | 22500 | 0.0156        | -                        |
+| 0.3567 | 23000 | 0.0132        | -                        |
+| 0.3645 | 23500 | 0.0152        | -                        |
+| 0.3723 | 24000 | 0.0147        | -                        |
+| 0.3800 | 24500 | 0.0142        | -                        |
+| 0.3878 | 25000 | 0.018         | -                        |
+| 0.3955 | 25500 | 0.0246        | -                        |
+| 0.4033 | 26000 | 0.0105        | -                        |
+| 0.4110 | 26500 | 0.0097        | -                        |
+| 0.4188 | 27000 | 0.0145        | -                        |
+| 0.4265 | 27500 | 0.0136        | -                        |
+| 0.4343 | 28000 | 0.0182        | -                        |
+| 0.4421 | 28500 | 0.016         | -                        |
+| 0.4498 | 29000 | 0.0088        | -                        |
+| 0.4576 | 29500 | 0.0106        | -                        |
+| 0.4653 | 30000 | 0.02          | -                        |
+| 0.4731 | 30500 | 0.0153        | -                        |
+| 0.4808 | 31000 | 0.0118        | -                        |
+| 0.4886 | 31500 | 0.0141        | -                        |
+| 0.4963 | 32000 | 0.0194        | -                        |
+| 0.5041 | 32500 | 0.0149        | -                        |
+| 0.5119 | 33000 | 0.0099        | -                        |
+| 0.5196 | 33500 | 0.0212        | -                        |
+| 0.5274 | 34000 | 0.0112        | -                        |
+| 0.5351 | 34500 | 0.0175        | -                        |
+| 0.5429 | 35000 | 0.0149        | -                        |
+| 0.5506 | 35500 | 0.0142        | -                        |
+| 0.5584 | 36000 | 0.0174        | -                        |
+| 0.5661 | 36500 | 0.0146        | -                        |
+| 0.5739 | 37000 | 0.0186        | -                        |
+| 0.5816 | 37500 | 0.0167        | -                        |
+| 0.5894 | 38000 | 0.0356        | -                        |
+| 0.5972 | 38500 | 0.0195        | -                        |
+| 0.6049 | 39000 | 0.0165        | -                        |
+| 0.6127 | 39500 | 0.0202        | -                        |
+| 0.6204 | 40000 | 0.0142        | -                        |
+| 0.6282 | 40500 | 0.0104        | -                        |
+| 0.6359 | 41000 | 0.0104        | -                        |
+| 0.6437 | 41500 | 0.0155        | -                        |
+| 0.6514 | 42000 | 0.0056        | -                        |
+| 0.6592 | 42500 | 0.0102        | -                        |
+| 0.6670 | 43000 | 0.0096        | -                        |
+| 0.6747 | 43500 | 0.0219        | -                        |
+| 0.6825 | 44000 | 0.0106        | -                        |
+| 0.6902 | 44500 | 0.0129        | -                        |
+| 0.6980 | 45000 | 0.0152        | -                        |
+| 0.7057 | 45500 | 0.0158        | -                        |
+| 0.7135 | 46000 | 0.0082        | -                        |
+| 0.7212 | 46500 | 0.0159        | -                        |
+| 0.7290 | 47000 | 0.0184        | -                        |
+| 0.7368 | 47500 | 0.0101        | -                        |
+| 0.7445 | 48000 | 0.0101        | -                        |
+| 0.7523 | 48500 | 0.0115        | -                        |
+| 0.7600 | 49000 | 0.0111        | -                        |
+| 0.7678 | 49500 | 0.0116        | -                        |
+| 0.7755 | 50000 | 0.0085        | 0.867                    |
+
+
+### Framework Versions
+- Python: 3.10.18
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.1
+- PyTorch: 2.7.1+cu126
+- Accelerate: 1.10.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.2
+
+## 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 +1,27 @@
+{
+  "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": "4.53.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.1",
+    "pytorch": "2.7.1+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/translation_evaluation_eval-en-sa_results.csv

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+epoch,steps,src2trg,trg2src
+1.0,64472,0.001,0.001
+2.0,128944,0.0,0.0
+3.0,193416,0.002,0.001
+4.0,257888,0.0,0.002
+5.0,322360,0.002,0.003
+6.0,386832,0.0,0.001
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+11.0,709192,0.002,0.002
+12.0,773664,0.001,0.0
+13.0,838136,0.0,0.0
+14.0,902608,0.001,0.004
+15.0,967080,0.001,0.001

model.safetensors

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+oid sha256:ae5042748107120afb38b17df9f52c3651ae1854fad9bf2a50ba4b1ce4ac82bb
+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

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
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+    "rstrip": false,
+    "single_word": false
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+    "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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+{
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+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
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+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}