Tamil embedding model v1

.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": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,554 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:92081
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: intfloat/multilingual-e5-base
+widget:
+- source_sentence: அவர் வீட்டுக்கு திரும்பினார்.அவர் தனது குரங்குக்கு உணவு கொடுத்து
+    சென்றார்.அவரின் குரங்கு எங்கும் காணப்படவில்லை.அவரின் குரங்கு எல்லையில் தேடி வந்தார்.அவருக்கு
+    அடுத்த நாள் தனது குரங்கு கண்டுபிடிக்க முடிந்தது.
+  sentences:
+  - Here Comes Santa Claus ஒரு இடத்தில் ஒரு முதல் 10 ஹெட்டாக இருந்தது
+  - சாம் ஒரு Pet Cat
+  - இது ஒரு ergonomic office chair.
+- source_sentence: 'Topics: ஏகத்துவத்தைக் கொண்டே பிரச்சாரத்தை ஆரம்பிக்க வேண்டும் and
+    தாயத்து கட்டுவது ஷிர்க்கை சார்ந்தது Begin propagation with Monotheism, and Using
+    amulets is Shirk Speaker: மவ்லவி கே.எல்.எம்.'
+  sentences:
+  - பிரெஞ்சுக்குத் தேவையான அளவு பிரெஞ்சு தேவை.
+  - அமெரிக்கா தான் மற்ற நாடுகள் கவனித்து வருகின்றன.
+  - ரஜினிகாந்த் ராகுல் ஒரு ராகுலக் காட்சியை வெளியிட்டிருக்கிறார்.
+- source_sentence: Karl & Co is a Norwegian situation comedy created by Tore Ryen,
+    starring Nils Vogt reprising his role as Karl Reverud from the popular sitcom
+    "Mot i brøstet".It aired on TV 2, run for three seasons from 1998 to 2001, a total
+    of 63 episodes.
+  sentences:
+  - ஆங்கிலத்தில் இதை Single Orgasm, Multiple Orgasm என்றும் கூறுகிறார்கள்.
+  - Hamvention 2018 Xenia இல் நடைபெறுகிறது.
+  - ஜூனியர் ஒப்பந்தங்கள்
+- source_sentence: There is only one temple in the village, no amman etc. The temple
+    to Sri Narayanan.கீழ்தட்டு மக்களே இராமனுஜரை, இவர்களுக்கு இருக்கும் பற்று எனக்கில்லையே
+    என நினைக்கவைத்த கதையும் உண்டு.ஒருநாள், நம்மாழ்வார் அவதரித்த ஊருக்குச் செல்லும்காலை,
+    அவருக்கு வழிதெரியவில்லை.
+  sentences:
+  - Wenham Parva ஒரு ஊர் மட்டுமே அல்ல, மேலும் ஒரு குடியரசு குடியரசு.
+  - பேச்சுவார்த்தை நிராகரிக்கப்படவில்லை.
+  - Zazie Beetz, Vanessa on Atlanta படத்தில் நடிக்கிறார்.
+- source_sentence: ஒரு முதியவன் பாதாளங்களைத் தாண்டும் தன் மந்திரக்கோலால் சாய்த்தபடியிருக்கிறான்
+    நாட்சத்திரங்களை...............................................................................................................................................................................
+    இது எத்தனையாவது [...]
+  sentences:
+  - விமானங்கள் போக்குவரத்துக்காக காவல்துறையில் அனுமதிக்கப்பட்டுள்ளன.
+  - தந்தைக்குக் கடினமான பரிசுகளைக் கொடுத்துக் கொண்டிருந்தார்.
+  - பிக்பாஸைப் பிடித்த போது எந்தப் படமும் நடக்கவில்லை.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 -->
+- **Maximum Sequence Length:** 512 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/huggingface/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': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (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("Tamil-ai/tamil-embed-base")
+# Run inference
+sentences = [
+    'ஒரு முதியவன் பாதாளங்களைத் தாண்டும் தன் மந்திரக்கோலால் சாய்த்தபடியிருக்கிறான் நாட்சத்திரங்களை............................................................................................................................................................................... இது எத்தனையாவது [...]',
+    'தந்தைக்குக் கடினமான பரிசுகளைக் கொடுத்துக் கொண்டிருந்தார்.',
+    'பிக்பாஸைப் பிடித்த போது எந்தப் படமும் நடக்கவில்லை.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.4205, 0.4317],
+#         [0.4205, 1.0000, 0.3737],
+#         [0.4317, 0.3737, 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.*
+-->
+
+<!--
+## 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: 92,081 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                              | positive                                                                          |
+  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                            |
+  | details | <ul><li>min: 15 tokens</li><li>mean: 57.89 tokens</li><li>max: 200 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.06 tokens</li><li>max: 87 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                                                                       | positive                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------|
+  | <code>Jack and Jill: A Village Story by Louisa May Alcott, is a children's book originally published in 1880.It takes place in a small New England town after the Civil War.The story of two good friends named Jack and Janey, "Jack and Jill" tells of the aftermath of a serious sliding accident.</code>                 | <code>ஜாக் மற்றும் ஜானி இரு நல்ல நண்பர்கள்.</code>    |
+  | <code>SourceMedia ஒரு mid-size diversified business-to-business digital media company owned by Observer Capital, which acquired the company from Investcorp in August 2014.Thomson Corporation's former Thomson Media division, SourceMedia விழுந்து, Thomson 2004 இல் Investcorp க்கு விற்கப்பட்டது $ 350 மில்லியன்.</code> | <code>SourceMedia ஒரு Digital Media நிறுவனம்</code>   |
+  | <code>ஒரு முதியவன் பாதாளங்களைத் தாண்டும் தன் மந்திரக்கோலால் சாய்த்தபடியிருக்கிறான் நாட்சத்திரங்களை............................................................................................................................................................................... இது எத்தனையாவது [...]</code>               | <code>பல்வேறு மாநிலங்களில் அரசுக்கு எச்சரிக்கை</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 1e-06
+- `warmup_steps`: 144
+- `fp16`: True
+- `gradient_checkpointing`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 64
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `learning_rate`: 1e-06
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 144
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 1
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: True
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `eval_strategy`: no
+- `per_device_eval_batch_size`: 8
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0174 | 25   | 9.5049        |
+| 0.0347 | 50   | 9.2988        |
+| 0.0521 | 75   | 8.7502        |
+| 0.0695 | 100  | 7.9748        |
+| 0.0869 | 125  | 7.1927        |
+| 0.1042 | 150  | 6.1935        |
+| 0.1216 | 175  | 5.3092        |
+| 0.1390 | 200  | 4.6630        |
+| 0.1564 | 225  | 4.1481        |
+| 0.1737 | 250  | 3.5569        |
+| 0.1911 | 275  | 3.5474        |
+| 0.2085 | 300  | 3.5098        |
+| 0.2259 | 325  | 3.2235        |
+| 0.2432 | 350  | 2.9600        |
+| 0.2606 | 375  | 3.0261        |
+| 0.2780 | 400  | 2.8874        |
+| 0.2953 | 425  | 2.9094        |
+| 0.3127 | 450  | 2.9079        |
+| 0.3301 | 475  | 2.6196        |
+| 0.3475 | 500  | 2.6887        |
+| 0.3648 | 525  | 3.0199        |
+| 0.3822 | 550  | 2.8014        |
+| 0.3996 | 575  | 2.8743        |
+| 0.4170 | 600  | 2.7243        |
+| 0.4343 | 625  | 2.7829        |
+| 0.4517 | 650  | 2.7898        |
+| 0.4691 | 675  | 2.7561        |
+| 0.4864 | 700  | 2.6587        |
+| 0.5038 | 725  | 2.6228        |
+| 0.5212 | 750  | 2.5352        |
+| 0.5386 | 775  | 2.6544        |
+| 0.5559 | 800  | 2.6122        |
+| 0.5733 | 825  | 2.6155        |
+| 0.5907 | 850  | 2.4361        |
+| 0.6081 | 875  | 2.6018        |
+| 0.6254 | 900  | 2.5225        |
+| 0.6428 | 925  | 2.5303        |
+| 0.6602 | 950  | 2.7318        |
+| 0.6776 | 975  | 2.5735        |
+| 0.6949 | 1000 | 2.5443        |
+| 0.7123 | 1025 | 2.3904        |
+| 0.7297 | 1050 | 2.4995        |
+| 0.7470 | 1075 | 2.5640        |
+| 0.7644 | 1100 | 2.6522        |
+| 0.7818 | 1125 | 2.5466        |
+| 0.7992 | 1150 | 2.4968        |
+| 0.8165 | 1175 | 2.3753        |
+| 0.8339 | 1200 | 2.4524        |
+| 0.8513 | 1225 | 2.3839        |
+| 0.8687 | 1250 | 2.6322        |
+| 0.8860 | 1275 | 2.5143        |
+| 0.9034 | 1300 | 2.6360        |
+| 0.9208 | 1325 | 2.3736        |
+| 0.9382 | 1350 | 3.3474        |
+| 0.9555 | 1375 | 4.2932        |
+| 0.9729 | 1400 | 3.8941        |
+| 0.9903 | 1425 | 4.0057        |
+| 1.0076 | 1450 | 3.2783        |
+| 1.0250 | 1475 | 2.6051        |
+| 1.0424 | 1500 | 2.8140        |
+| 1.0598 | 1525 | 2.4573        |
+| 1.0771 | 1550 | 2.5487        |
+| 1.0945 | 1575 | 2.5347        |
+| 1.1119 | 1600 | 2.3618        |
+| 1.1293 | 1625 | 2.3501        |
+| 1.1466 | 1650 | 2.4186        |
+| 1.1640 | 1675 | 2.3757        |
+| 1.1814 | 1700 | 2.6012        |
+| 1.1987 | 1725 | 2.3281        |
+| 1.2161 | 1750 | 2.4444        |
+| 1.2335 | 1775 | 2.5461        |
+| 1.2509 | 1800 | 2.5203        |
+| 1.2682 | 1825 | 2.4201        |
+| 1.2856 | 1850 | 2.6096        |
+| 1.3030 | 1875 | 2.4021        |
+| 1.3204 | 1900 | 2.4524        |
+| 1.3377 | 1925 | 2.3002        |
+| 1.3551 | 1950 | 2.4063        |
+| 1.3725 | 1975 | 2.1237        |
+| 1.3899 | 2000 | 2.3219        |
+| 1.4072 | 2025 | 2.3227        |
+| 1.4246 | 2050 | 2.3646        |
+| 1.4420 | 2075 | 2.4407        |
+| 1.4593 | 2100 | 2.2862        |
+| 1.4767 | 2125 | 2.2900        |
+| 1.4941 | 2150 | 2.2512        |
+| 1.5115 | 2175 | 2.3741        |
+| 1.5288 | 2200 | 2.6308        |
+| 1.5462 | 2225 | 2.5161        |
+| 1.5636 | 2250 | 2.4871        |
+| 1.5810 | 2275 | 2.5049        |
+| 1.5983 | 2300 | 2.6384        |
+| 1.6157 | 2325 | 2.4185        |
+| 1.6331 | 2350 | 2.4573        |
+| 1.6505 | 2375 | 2.2954        |
+| 1.6678 | 2400 | 2.2384        |
+| 1.6852 | 2425 | 2.3318        |
+| 1.7026 | 2450 | 2.2915        |
+| 1.7199 | 2475 | 2.2013        |
+| 1.7373 | 2500 | 2.4082        |
+| 1.7547 | 2525 | 2.5290        |
+| 1.7721 | 2550 | 2.4825        |
+| 1.7894 | 2575 | 2.4610        |
+| 1.8068 | 2600 | 2.3414        |
+| 1.8242 | 2625 | 2.3729        |
+| 1.8416 | 2650 | 2.5862        |
+| 1.8589 | 2675 | 2.4320        |
+| 1.8763 | 2700 | 2.2745        |
+| 1.8937 | 2725 | 2.3046        |
+| 1.9110 | 2750 | 2.3621        |
+| 1.9284 | 2775 | 2.3097        |
+| 1.9458 | 2800 | 4.1645        |
+| 1.9632 | 2825 | 4.5466        |
+| 1.9805 | 2850 | 4.6750        |
+| 1.9979 | 2875 | 2.8955        |
+| 2.0153 | 2900 | 2.9962        |
+| 2.0327 | 2925 | 2.3366        |
+| 2.0500 | 2950 | 2.2591        |
+| 2.0674 | 2975 | 2.3375        |
+| 2.0848 | 3000 | 2.4169        |
+| 2.1022 | 3025 | 2.2635        |
+| 2.1195 | 3050 | 2.1642        |
+| 2.1369 | 3075 | 2.4082        |
+| 2.1543 | 3100 | 2.3501        |
+| 2.1716 | 3125 | 2.4870        |
+| 2.1890 | 3150 | 2.7393        |
+| 2.2064 | 3175 | 2.3203        |
+| 2.2238 | 3200 | 2.2731        |
+| 2.2411 | 3225 | 2.1901        |
+| 2.2585 | 3250 | 2.3000        |
+| 2.2759 | 3275 | 2.3846        |
+| 2.2933 | 3300 | 2.2514        |
+| 2.3106 | 3325 | 2.2218        |
+| 2.3280 | 3350 | 2.5800        |
+| 2.3454 | 3375 | 2.4384        |
+| 2.3628 | 3400 | 2.4946        |
+| 2.3801 | 3425 | 2.2781        |
+| 2.3975 | 3450 | 2.2777        |
+| 2.4149 | 3475 | 2.2062        |
+| 2.4322 | 3500 | 2.3994        |
+| 2.4496 | 3525 | 2.5084        |
+| 2.4670 | 3550 | 2.1158        |
+| 2.4844 | 3575 | 2.0865        |
+| 2.5017 | 3600 | 2.3174        |
+| 2.5191 | 3625 | 2.3668        |
+| 2.5365 | 3650 | 2.3439        |
+| 2.5539 | 3675 | 2.4482        |
+| 2.5712 | 3700 | 2.3998        |
+| 2.5886 | 3725 | 2.2155        |
+| 2.6060 | 3750 | 2.0207        |
+| 2.6233 | 3775 | 2.2652        |
+| 2.6407 | 3800 | 2.4261        |
+| 2.6581 | 3825 | 2.2214        |
+| 2.6755 | 3850 | 2.2244        |
+| 2.6928 | 3875 | 2.2835        |
+| 2.7102 | 3900 | 2.4259        |
+| 2.7276 | 3925 | 2.3013        |
+| 2.7450 | 3950 | 2.1069        |
+| 2.7623 | 3975 | 2.4415        |
+| 2.7797 | 4000 | 2.3380        |
+| 2.7971 | 4025 | 2.3013        |
+| 2.8145 | 4050 | 2.4202        |
+| 2.8318 | 4075 | 2.2488        |
+| 2.8492 | 4100 | 2.1855        |
+| 2.8666 | 4125 | 2.3882        |
+| 2.8839 | 4150 | 2.5306        |
+| 2.9013 | 4175 | 2.3197        |
+| 2.9187 | 4200 | 2.3295        |
+| 2.9361 | 4225 | 3.2070        |
+| 2.9534 | 4250 | 3.9697        |
+| 2.9708 | 4275 | 4.2241        |
+| 2.9882 | 4300 | 3.5779        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.2.3
+- Transformers: 5.3.0
+- PyTorch: 2.9.0+cu126
+- Accelerate: 1.12.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "5.3.0",
+    "pytorch": "2.9.0+cu126"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a3d1a46a3382881a1ea94737b0850bbad83124b072227be6e57301971938e89
+size 1112197064

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
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
+size 17082987

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}