Add new SentenceTransformer model.

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

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+---
+language: []
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:1K<n<10K
+- loss:CosineSimilarityLoss
+base_model: Rajan/NepaliBERT
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: अघिल्लो वर्ष देखि।
+  sentences:
+  - अघिल्लो वर्ष देखि .।
+  - एउटी महिला बन्दुक हान्दै छिन्।
+  - हिउँमा हिंडिरहेको सेतो कुकुर।
+- source_sentence: यो मोलोच दृश्य हो।
+  sentences:
+  - वास्तवमा, यो केवल डच हो।
+  - एउटा मानिस डोरीमा झुलिरहेको छ।
+  - रातो झोला लिएर सडकमा उभिएकी केटी।
+- source_sentence: दमास्कसमा रुसीहरू!
+  sentences:
+  - रुसीहरू दमस्कसमा किन छन्?
+  - कसैले मिर्चको बीउ निकाल्दै छ।
+  - एकजना मानिस साइकल चलाउँदै छन्।
+- source_sentence: रेल ट्र्याकमा रेल।
+  sentences:
+  - लामो रेल रेल ट्र्याकमा छ।
+  - एउटी महिला सिडु चढिरहेकी छिन्।
+  - एक व्यक्ति सडकमा हिर्किरहेको छ।
+- source_sentence: रातो, डबल डेकर बस।
+  sentences:
+  - रातो डबल डेकर बस।
+  - दुई कालो कुकुर हिउँमा हिंड्दै।
+  - एउटी महिला मासु फ्राइरहेकी छिन्।
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on Rajan/NepaliBERT
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: stsb dev nepali
+      type: stsb-dev-nepali
+    metrics:
+    - type: pearson_cosine
+      value: 0.6971387543395983
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.6623150295431888
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.6332077130918778
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.6078651194262178
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.6339817618698202
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.6090065238762821
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.4848273995348276
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.5306425402414711
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.6971387543395983
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.6623150295431888
+      name: Spearman Max
+---
+
+# SentenceTransformer based on Rajan/NepaliBERT
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Rajan/NepaliBERT](https://huggingface.co/Rajan/NepaliBERT). 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:** [Rajan/NepaliBERT](https://huggingface.co/Rajan/NepaliBERT) <!-- at revision 996c3b86b779a63225b473221678447c9d9185d0 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **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}) with Transformer model: BertModel 
+  (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})
+)
+```
+
+## 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("syubraj/sentenceTransformer_nepali_new")
+# Run inference
+sentences = [
+    'रातो, डबल डेकर बस।',
+    'रातो डबल डेकर बस।',
+    'दुई कालो कुकुर हिउँमा हिंड्दै।',
+]
+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
+
+#### Semantic Similarity
+* Dataset: `stsb-dev-nepali`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| pearson_cosine     | 0.6971     |
+| spearman_cosine    | 0.6623     |
+| pearson_manhattan  | 0.6332     |
+| spearman_manhattan | 0.6079     |
+| pearson_euclidean  | 0.634      |
+| spearman_euclidean | 0.609      |
+| pearson_dot        | 0.4848     |
+| spearman_dot       | 0.5306     |
+| pearson_max        | 0.6971     |
+| **spearman_max**   | **0.6623** |
+
+<!--
+## 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: 4,599 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                        | label                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 19.5 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.43 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                               | sentence_1                                                     | label                           |
+  |:-------------------------------------------------------------------------|:---------------------------------------------------------------|:--------------------------------|
+  | <code>एक व्यक्ति प्याज काट्दै छ।</code>                                  | <code>एउटा बिरालो शौचालयमा पपिङ गर्दैछ।</code>                 | <code>0.0</code>                |
+  | <code>क्यानडाको तेल रेल विस्फोटमा थप मृत्यु हुने अपेक्षा गरिएको छ</code> | <code>क्यानडामा रेल दुर्घटनामा पाँच जनाको मृत्यु भएको छ</code> | <code>0.5599999904632569</code> |
+  | <code>एउटी महिला झिंगा माझ्दै छिन्।</code>                               | <code>एउटी महिला केही झिंगा माझ्दै।</code>                     | <code>1.0</code>                |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 100
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 100
+- `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
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step  | Training Loss | stsb-dev-nepali_spearman_max |
+|:-------:|:-----:|:-------------:|:----------------------------:|
+| 1.0     | 288   | -             | 0.5355                       |
+| 1.7361  | 500   | 0.0723        | -                            |
+| 2.0     | 576   | -             | 0.5794                       |
+| 3.0     | 864   | -             | 0.6108                       |
+| 3.4722  | 1000  | 0.047         | 0.6147                       |
+| 4.0     | 1152  | -             | 0.6259                       |
+| 5.0     | 1440  | -             | 0.6356                       |
+| 5.2083  | 1500  | 0.034         | -                            |
+| 6.0     | 1728  | -             | 0.6329                       |
+| 6.9444  | 2000  | 0.0217        | 0.6375                       |
+| 7.0     | 2016  | -             | 0.6382                       |
+| 8.0     | 2304  | -             | 0.6468                       |
+| 8.6806  | 2500  | 0.0137        | -                            |
+| 9.0     | 2592  | -             | 0.6348                       |
+| 10.0    | 2880  | -             | 0.6332                       |
+| 10.4167 | 3000  | 0.0102        | 0.6427                       |
+| 11.0    | 3168  | -             | 0.6370                       |
+| 12.0    | 3456  | -             | 0.6515                       |
+| 12.1528 | 3500  | 0.0084        | -                            |
+| 13.0    | 3744  | -             | 0.6546                       |
+| 13.8889 | 4000  | 0.0069        | 0.6400                       |
+| 14.0    | 4032  | -             | 0.6610                       |
+| 15.0    | 4320  | -             | 0.6495                       |
+| 15.625  | 4500  | 0.006         | -                            |
+| 16.0    | 4608  | -             | 0.6574                       |
+| 17.0    | 4896  | -             | 0.6486                       |
+| 17.3611 | 5000  | 0.0053        | 0.6589                       |
+| 18.0    | 5184  | -             | 0.6592                       |
+| 19.0    | 5472  | -             | 0.6488                       |
+| 19.0972 | 5500  | 0.0047        | -                            |
+| 20.0    | 5760  | -             | 0.6436                       |
+| 20.8333 | 6000  | 0.0044        | 0.6576                       |
+| 21.0    | 6048  | -             | 0.6515                       |
+| 22.0    | 6336  | -             | 0.6541                       |
+| 22.5694 | 6500  | 0.0041        | -                            |
+| 23.0    | 6624  | -             | 0.6549                       |
+| 24.0    | 6912  | -             | 0.6571                       |
+| 24.3056 | 7000  | 0.0037        | 0.6603                       |
+| 25.0    | 7200  | -             | 0.6699                       |
+| 26.0    | 7488  | -             | 0.6653                       |
+| 26.0417 | 7500  | 0.0037        | -                            |
+| 27.0    | 7776  | -             | 0.6609                       |
+| 27.7778 | 8000  | 0.0033        | 0.6578                       |
+| 28.0    | 8064  | -             | 0.6606                       |
+| 29.0    | 8352  | -             | 0.6614                       |
+| 29.5139 | 8500  | 0.0031        | -                            |
+| 30.0    | 8640  | -             | 0.6579                       |
+| 31.0    | 8928  | -             | 0.6688                       |
+| 31.25   | 9000  | 0.0028        | 0.6650                       |
+| 32.0    | 9216  | -             | 0.6639                       |
+| 32.9861 | 9500  | 0.0027        | -                            |
+| 33.0    | 9504  | -             | 0.6624                       |
+| 34.0    | 9792  | -             | 0.6646                       |
+| 34.7222 | 10000 | 0.0025        | 0.6530                       |
+| 35.0    | 10080 | -             | 0.6587                       |
+| 36.0    | 10368 | -             | 0.6671                       |
+| 36.4583 | 10500 | 0.0025        | -                            |
+| 37.0    | 10656 | -             | 0.6614                       |
+| 38.0    | 10944 | -             | 0.6602                       |
+| 38.1944 | 11000 | 0.0024        | 0.6576                       |
+| 39.0    | 11232 | -             | 0.6665                       |
+| 39.9306 | 11500 | 0.0023        | -                            |
+| 40.0    | 11520 | -             | 0.6663                       |
+| 41.0    | 11808 | -             | 0.6734                       |
+| 41.6667 | 12000 | 0.0021        | 0.6633                       |
+| 42.0    | 12096 | -             | 0.6667                       |
+| 43.0    | 12384 | -             | 0.6679                       |
+| 43.4028 | 12500 | 0.002         | -                            |
+| 44.0    | 12672 | -             | 0.6701                       |
+| 45.0    | 12960 | -             | 0.6650                       |
+| 45.1389 | 13000 | 0.0019        | 0.6680                       |
+| 46.0    | 13248 | -             | 0.6631                       |
+| 46.875  | 13500 | 0.0018        | -                            |
+| 47.0    | 13536 | -             | 0.6643                       |
+| 48.0    | 13824 | -             | 0.6631                       |
+| 48.6111 | 14000 | 0.0017        | 0.6648                       |
+| 49.0    | 14112 | -             | 0.6648                       |
+| 50.0    | 14400 | -             | 0.6619                       |
+| 50.3472 | 14500 | 0.0017        | -                            |
+| 51.0    | 14688 | -             | 0.6633                       |
+| 52.0    | 14976 | -             | 0.6622                       |
+| 52.0833 | 15000 | 0.0016        | 0.6612                       |
+| 53.0    | 15264 | -             | 0.6670                       |
+| 53.8194 | 15500 | 0.0015        | -                            |
+| 54.0    | 15552 | -             | 0.6618                       |
+| 55.0    | 15840 | -             | 0.6641                       |
+| 55.5556 | 16000 | 0.0015        | 0.6617                       |
+| 56.0    | 16128 | -             | 0.6669                       |
+| 57.0    | 16416 | -             | 0.6645                       |
+| 57.2917 | 16500 | 0.0014        | -                            |
+| 58.0    | 16704 | -             | 0.6642                       |
+| 59.0    | 16992 | -             | 0.6579                       |
+| 59.0278 | 17000 | 0.0013        | 0.6592                       |
+| 60.0    | 17280 | -             | 0.6589                       |
+| 60.7639 | 17500 | 0.0014        | -                            |
+| 61.0    | 17568 | -             | 0.6685                       |
+| 62.0    | 17856 | -             | 0.6673                       |
+| 62.5    | 18000 | 0.0012        | 0.6669                       |
+| 63.0    | 18144 | -             | 0.6665                       |
+| 64.0    | 18432 | -             | 0.6626                       |
+| 64.2361 | 18500 | 0.0012        | -                            |
+| 65.0    | 18720 | -             | 0.6619                       |
+| 65.9722 | 19000 | 0.0012        | 0.6643                       |
+| 66.0    | 19008 | -             | 0.6651                       |
+| 67.0    | 19296 | -             | 0.6628                       |
+| 67.7083 | 19500 | 0.0011        | -                            |
+| 68.0    | 19584 | -             | 0.6658                       |
+| 69.0    | 19872 | -             | 0.6615                       |
+| 69.4444 | 20000 | 0.0011        | 0.6627                       |
+| 70.0    | 20160 | -             | 0.6657                       |
+| 71.0    | 20448 | -             | 0.6663                       |
+| 71.1806 | 20500 | 0.0011        | -                            |
+| 72.0    | 20736 | -             | 0.6634                       |
+| 72.9167 | 21000 | 0.001         | 0.6649                       |
+| 73.0    | 21024 | -             | 0.6632                       |
+| 74.0    | 21312 | -             | 0.6658                       |
+| 74.6528 | 21500 | 0.001         | -                            |
+| 75.0    | 21600 | -             | 0.6639                       |
+| 76.0    | 21888 | -             | 0.6601                       |
+| 76.3889 | 22000 | 0.001         | 0.6623                       |
+| 77.0    | 22176 | -             | 0.6607                       |
+| 78.0    | 22464 | -             | 0.6613                       |
+| 78.125  | 22500 | 0.0009        | -                            |
+| 79.0    | 22752 | -             | 0.6613                       |
+| 79.8611 | 23000 | 0.0009        | 0.6615                       |
+| 80.0    | 23040 | -             | 0.6615                       |
+| 81.0    | 23328 | -             | 0.6617                       |
+| 81.5972 | 23500 | 0.0008        | -                            |
+| 82.0    | 23616 | -             | 0.6604                       |
+| 83.0    | 23904 | -             | 0.6605                       |
+| 83.3333 | 24000 | 0.0008        | 0.6602                       |
+| 84.0    | 24192 | -             | 0.6628                       |
+| 85.0    | 24480 | -             | 0.6603                       |
+| 85.0694 | 24500 | 0.0008        | -                            |
+| 86.0    | 24768 | -             | 0.6602                       |
+| 86.8056 | 25000 | 0.0008        | 0.6592                       |
+| 87.0    | 25056 | -             | 0.6611                       |
+| 88.0    | 25344 | -             | 0.6612                       |
+| 88.5417 | 25500 | 0.0008        | -                            |
+| 89.0    | 25632 | -             | 0.6607                       |
+| 90.0    | 25920 | -             | 0.6598                       |
+| 90.2778 | 26000 | 0.0008        | 0.6607                       |
+| 91.0    | 26208 | -             | 0.6615                       |
+| 92.0    | 26496 | -             | 0.6615                       |
+| 92.0139 | 26500 | 0.0007        | -                            |
+| 93.0    | 26784 | -             | 0.6609                       |
+| 93.75   | 27000 | 0.0007        | 0.6607                       |
+| 94.0    | 27072 | -             | 0.6612                       |
+| 95.0    | 27360 | -             | 0.6624                       |
+| 95.4861 | 27500 | 0.0007        | -                            |
+| 96.0    | 27648 | -             | 0.6627                       |
+| 97.0    | 27936 | -             | 0.6618                       |
+| 97.2222 | 28000 | 0.0007        | 0.6619                       |
+| 98.0    | 28224 | -             | 0.6621                       |
+| 98.9583 | 28500 | 0.0007        | -                            |
+| 99.0    | 28512 | -             | 0.6623                       |
+| 100.0   | 28800 | -             | 0.6623                       |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.0
+- Transformers: 4.41.2
+- PyTorch: 2.1.2
+- Accelerate: 0.30.1
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## 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 +1,26 @@
+{
+  "_name_or_path": "Rajan/NepaliBERT",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.0",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1067c8f2fe7b9c8e13cb5dbbbd9d643db0b81fb45b58bcf76709ff1273ee6b1f
+size 327667976

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}