pritamdeka
commited on
Commit
•
6c6a797
1
Parent(s):
f170acf
Add new SentenceTransformer model.
Browse files- 1_Pooling/config.json +10 -0
- README.md +459 -0
- config.json +26 -0
- config_sentence_transformers.json +10 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +65 -0
- vocab.txt +0 -0
1_Pooling/config.json
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@@ -0,0 +1,10 @@
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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---
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base_model: pritamdeka/assamese-bert-nli-v2
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datasets: []
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language: []
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library_name: sentence-transformers
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metrics:
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- pearson_cosine
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- spearman_cosine
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- pearson_manhattan
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- spearman_manhattan
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- pearson_euclidean
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- spearman_euclidean
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- pearson_dot
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- spearman_dot
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- pearson_max
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- spearman_max
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:5749
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- loss:CosineSimilarityLoss
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widget:
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- source_sentence: আমি "... comoving মহাজাগতিক বিশ্ৰাম ফ্ৰেমৰ তুলনাত ... সিংহ নক্ষত্ৰমণ্ডলৰ
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ফালে কিছু 371 কিলোমিটাৰ প্ৰতি ছেকেণ্ডত" আগবাঢ়িছো.
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sentences:
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- বাস্কেটবল খেলুৱৈগৰাকীয়ে নিজৰ দলৰ হৈ পইণ্ট লাভ কৰিবলৈ ওলাইছে।
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- আন কোনো বস্তুৰ লগত আপেক্ষিক নহোৱা কোনো ‘ষ্টিল’ নাই।
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- এজনী ছোৱালীয়ে বতাহ বাদ্যযন্ত্ৰ বজায়।
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- source_sentence: চাৰিটা ল’ৰা-ছোৱালীয়ে ভঁৰালৰ জীৱ-জন্তুবোৰলৈ চাই আছে।
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sentences:
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- ডাইনিং টেবুল এখনৰ চাৰিওফালে বৃদ্ধৰ দল এটাই পোজ দিছে।
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- বিকিনি পিন্ধা চাৰিগৰাকী মহিলাই বিলত ভলীবল খেলি আছে।
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- ল’ৰা-ছোৱালীয়ে ভেড়া চাই।
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- source_sentence: ডালত বহি থকা দুটা টান ঈগল।
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sentences:
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- জাতৰ জেব্ৰা ডানিঅ’ অত্যন্ত কঠোৰ মাছ, ইহঁতক হত্যা কৰাটো প্ৰায় কঠিন।
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- এটা ডালত দুটা ঈগল বহি আছে।
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- নূন্যতম মজুৰিৰ আইনসমূহে কম দক্ষ, কম উৎপাদনশীল লোকক আটাইতকৈ বেছি আঘাত দিয়ে।
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- source_sentence: '"মই আচলতে যি বিচাৰিছো সেয়া হৈছে মুছলমান জনসংখ্যাৰ এটা অনুমান..."
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@ThanosK আৰু @T.E.D., এটা সামগ্ৰিক, সাধাৰণ জনসংখ্যাৰ অনুমান f.e.'
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sentences:
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- এগৰাকী মহিলাই সেউজীয়া পিঁয়াজ কাটি আছে।
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- তলত দিয়া কথাখিনি মোৰ কুকুৰ কাণৰ দৰে কপিৰ পৰা লোৱা হৈছে নিউ পেংগুইন এটলাছ অৱ মেডিভেল
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হিষ্ট্ৰীৰ।
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- আমাৰ দৰে সৌৰজগতৰ কোনো তাৰকাৰাজ্যৰ বাহিৰত থকাটো সম্ভৱ হ’ব পাৰে।
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- source_sentence: ইণ্টাৰনেট কেমেৰাৰ জৰিয়তে এগৰাকী ছোৱালীৰ লগত কথা পাতিলে মানুহজনে।
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sentences:
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- গছৰ শাৰী এটাৰ সন্মুখত পথাৰত ভেড়া চৰিছে।
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- এজন মানুহে গীটাৰ বজাই আছে।
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- ৱেবকেমৰ জৰিয়তে এগৰাকী ছোৱালীৰ সৈতে কথা পাতিছে এজন কিশোৰে।
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model-index:
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- name: SentenceTransformer based on pritamdeka/assamese-bert-nli-v2
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results:
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- task:
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type: semantic-similarity
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name: Semantic Similarity
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dataset:
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name: pritamdeka/stsb assamese translated dev
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type: pritamdeka/stsb-assamese-translated-dev
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metrics:
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- type: pearson_cosine
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value: 0.8582086169969396
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.8558833817052474
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name: Spearman Cosine
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- type: pearson_manhattan
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value: 0.8402288134127139
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.8466669319881411
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name: Spearman Manhattan
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- type: pearson_euclidean
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value: 0.8401702610820984
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.846937443225358
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name: Spearman Euclidean
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- type: pearson_dot
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value: 0.8293854931734366
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name: Pearson Dot
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- type: spearman_dot
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value: 0.8279065905764471
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name: Spearman Dot
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- type: pearson_max
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value: 0.8582086169969396
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name: Pearson Max
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- type: spearman_max
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value: 0.8558833817052474
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name: Spearman Max
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- task:
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type: semantic-similarity
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name: Semantic Similarity
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dataset:
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name: pritamdeka/stsb assamese translated test
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type: pritamdeka/stsb-assamese-translated-test
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metrics:
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- type: pearson_cosine
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value: 0.8231106499789409
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.8235370017309012
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name: Spearman Cosine
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- type: pearson_manhattan
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value: 0.8131384280231726
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.817044158823682
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name: Spearman Manhattan
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- type: pearson_euclidean
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value: 0.8132779879142208
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.8170404249477559
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name: Spearman Euclidean
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- type: pearson_dot
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value: 0.7896666837864712
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name: Pearson Dot
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- type: spearman_dot
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value: 0.7870703093898731
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name: Spearman Dot
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- type: pearson_max
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value: 0.8231106499789409
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name: Pearson Max
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- type: spearman_max
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value: 0.8235370017309012
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name: Spearman Max
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---
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# SentenceTransformer based on pritamdeka/assamese-bert-nli-v2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pritamdeka/assamese-bert-nli-v2](https://huggingface.co/pritamdeka/assamese-bert-nli-v2). 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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [pritamdeka/assamese-bert-nli-v2](https://huggingface.co/pritamdeka/assamese-bert-nli-v2) <!-- at revision 1f2c1246128304aed5ae3fd6ab1b2b4be69462f2 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 tokens
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
|
163 |
+
|
164 |
+
## Usage
|
165 |
+
|
166 |
+
### Direct Usage (Sentence Transformers)
|
167 |
+
|
168 |
+
First install the Sentence Transformers library:
|
169 |
+
|
170 |
+
```bash
|
171 |
+
pip install -U sentence-transformers
|
172 |
+
```
|
173 |
+
|
174 |
+
Then you can load this model and run inference.
|
175 |
+
```python
|
176 |
+
from sentence_transformers import SentenceTransformer
|
177 |
+
|
178 |
+
# Download from the 🤗 Hub
|
179 |
+
model = SentenceTransformer("pritamdeka/assamese-bert-nli-v2-assamese-sts")
|
180 |
+
# Run inference
|
181 |
+
sentences = [
|
182 |
+
'ইণ্টাৰনেট কেমেৰাৰ জৰিয়তে এগৰাকী ছোৱালীৰ লগত কথা পাতিলে মানুহজনে।',
|
183 |
+
'ৱেবকেমৰ জৰিয়তে এগৰাকী ছোৱালীৰ সৈতে কথা পাতিছে এজন কিশোৰে।',
|
184 |
+
'এজন মানুহে গীটাৰ বজাই আছে।',
|
185 |
+
]
|
186 |
+
embeddings = model.encode(sentences)
|
187 |
+
print(embeddings.shape)
|
188 |
+
# [3, 768]
|
189 |
+
|
190 |
+
# Get the similarity scores for the embeddings
|
191 |
+
similarities = model.similarity(embeddings, embeddings)
|
192 |
+
print(similarities.shape)
|
193 |
+
# [3, 3]
|
194 |
+
```
|
195 |
+
|
196 |
+
<!--
|
197 |
+
### Direct Usage (Transformers)
|
198 |
+
|
199 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
200 |
+
|
201 |
+
</details>
|
202 |
+
-->
|
203 |
+
|
204 |
+
<!--
|
205 |
+
### Downstream Usage (Sentence Transformers)
|
206 |
+
|
207 |
+
You can finetune this model on your own dataset.
|
208 |
+
|
209 |
+
<details><summary>Click to expand</summary>
|
210 |
+
|
211 |
+
</details>
|
212 |
+
-->
|
213 |
+
|
214 |
+
<!--
|
215 |
+
### Out-of-Scope Use
|
216 |
+
|
217 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
218 |
+
-->
|
219 |
+
|
220 |
+
## Evaluation
|
221 |
+
|
222 |
+
### Metrics
|
223 |
+
|
224 |
+
#### Semantic Similarity
|
225 |
+
* Dataset: `pritamdeka/stsb-assamese-translated-dev`
|
226 |
+
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
227 |
+
|
228 |
+
| Metric | Value |
|
229 |
+
|:--------------------|:-----------|
|
230 |
+
| pearson_cosine | 0.8582 |
|
231 |
+
| **spearman_cosine** | **0.8559** |
|
232 |
+
| pearson_manhattan | 0.8402 |
|
233 |
+
| spearman_manhattan | 0.8467 |
|
234 |
+
| pearson_euclidean | 0.8402 |
|
235 |
+
| spearman_euclidean | 0.8469 |
|
236 |
+
| pearson_dot | 0.8294 |
|
237 |
+
| spearman_dot | 0.8279 |
|
238 |
+
| pearson_max | 0.8582 |
|
239 |
+
| spearman_max | 0.8559 |
|
240 |
+
|
241 |
+
#### Semantic Similarity
|
242 |
+
* Dataset: `pritamdeka/stsb-assamese-translated-test`
|
243 |
+
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
244 |
+
|
245 |
+
| Metric | Value |
|
246 |
+
|:--------------------|:-----------|
|
247 |
+
| pearson_cosine | 0.8231 |
|
248 |
+
| **spearman_cosine** | **0.8235** |
|
249 |
+
| pearson_manhattan | 0.8131 |
|
250 |
+
| spearman_manhattan | 0.817 |
|
251 |
+
| pearson_euclidean | 0.8133 |
|
252 |
+
| spearman_euclidean | 0.817 |
|
253 |
+
| pearson_dot | 0.7897 |
|
254 |
+
| spearman_dot | 0.7871 |
|
255 |
+
| pearson_max | 0.8231 |
|
256 |
+
| spearman_max | 0.8235 |
|
257 |
+
|
258 |
+
<!--
|
259 |
+
## Bias, Risks and Limitations
|
260 |
+
|
261 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
262 |
+
-->
|
263 |
+
|
264 |
+
<!--
|
265 |
+
### Recommendations
|
266 |
+
|
267 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
268 |
+
-->
|
269 |
+
|
270 |
+
## Training Details
|
271 |
+
|
272 |
+
### Training Hyperparameters
|
273 |
+
#### Non-Default Hyperparameters
|
274 |
+
|
275 |
+
- `eval_strategy`: steps
|
276 |
+
- `per_device_train_batch_size`: 16
|
277 |
+
- `per_device_eval_batch_size`: 16
|
278 |
+
- `num_train_epochs`: 4
|
279 |
+
- `warmup_ratio`: 0.1
|
280 |
+
- `fp16`: True
|
281 |
+
|
282 |
+
#### All Hyperparameters
|
283 |
+
<details><summary>Click to expand</summary>
|
284 |
+
|
285 |
+
- `overwrite_output_dir`: False
|
286 |
+
- `do_predict`: False
|
287 |
+
- `eval_strategy`: steps
|
288 |
+
- `prediction_loss_only`: True
|
289 |
+
- `per_device_train_batch_size`: 16
|
290 |
+
- `per_device_eval_batch_size`: 16
|
291 |
+
- `per_gpu_train_batch_size`: None
|
292 |
+
- `per_gpu_eval_batch_size`: None
|
293 |
+
- `gradient_accumulation_steps`: 1
|
294 |
+
- `eval_accumulation_steps`: None
|
295 |
+
- `learning_rate`: 5e-05
|
296 |
+
- `weight_decay`: 0.0
|
297 |
+
- `adam_beta1`: 0.9
|
298 |
+
- `adam_beta2`: 0.999
|
299 |
+
- `adam_epsilon`: 1e-08
|
300 |
+
- `max_grad_norm`: 1.0
|
301 |
+
- `num_train_epochs`: 4
|
302 |
+
- `max_steps`: -1
|
303 |
+
- `lr_scheduler_type`: linear
|
304 |
+
- `lr_scheduler_kwargs`: {}
|
305 |
+
- `warmup_ratio`: 0.1
|
306 |
+
- `warmup_steps`: 0
|
307 |
+
- `log_level`: passive
|
308 |
+
- `log_level_replica`: warning
|
309 |
+
- `log_on_each_node`: True
|
310 |
+
- `logging_nan_inf_filter`: True
|
311 |
+
- `save_safetensors`: True
|
312 |
+
- `save_on_each_node`: False
|
313 |
+
- `save_only_model`: False
|
314 |
+
- `restore_callback_states_from_checkpoint`: False
|
315 |
+
- `no_cuda`: False
|
316 |
+
- `use_cpu`: False
|
317 |
+
- `use_mps_device`: False
|
318 |
+
- `seed`: 42
|
319 |
+
- `data_seed`: None
|
320 |
+
- `jit_mode_eval`: False
|
321 |
+
- `use_ipex`: False
|
322 |
+
- `bf16`: False
|
323 |
+
- `fp16`: True
|
324 |
+
- `fp16_opt_level`: O1
|
325 |
+
- `half_precision_backend`: auto
|
326 |
+
- `bf16_full_eval`: False
|
327 |
+
- `fp16_full_eval`: False
|
328 |
+
- `tf32`: None
|
329 |
+
- `local_rank`: 0
|
330 |
+
- `ddp_backend`: None
|
331 |
+
- `tpu_num_cores`: None
|
332 |
+
- `tpu_metrics_debug`: False
|
333 |
+
- `debug`: []
|
334 |
+
- `dataloader_drop_last`: False
|
335 |
+
- `dataloader_num_workers`: 0
|
336 |
+
- `dataloader_prefetch_factor`: None
|
337 |
+
- `past_index`: -1
|
338 |
+
- `disable_tqdm`: False
|
339 |
+
- `remove_unused_columns`: True
|
340 |
+
- `label_names`: None
|
341 |
+
- `load_best_model_at_end`: False
|
342 |
+
- `ignore_data_skip`: False
|
343 |
+
- `fsdp`: []
|
344 |
+
- `fsdp_min_num_params`: 0
|
345 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
346 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
347 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
348 |
+
- `deepspeed`: None
|
349 |
+
- `label_smoothing_factor`: 0.0
|
350 |
+
- `optim`: adamw_torch
|
351 |
+
- `optim_args`: None
|
352 |
+
- `adafactor`: False
|
353 |
+
- `group_by_length`: False
|
354 |
+
- `length_column_name`: length
|
355 |
+
- `ddp_find_unused_parameters`: None
|
356 |
+
- `ddp_bucket_cap_mb`: None
|
357 |
+
- `ddp_broadcast_buffers`: False
|
358 |
+
- `dataloader_pin_memory`: True
|
359 |
+
- `dataloader_persistent_workers`: False
|
360 |
+
- `skip_memory_metrics`: True
|
361 |
+
- `use_legacy_prediction_loop`: False
|
362 |
+
- `push_to_hub`: False
|
363 |
+
- `resume_from_checkpoint`: None
|
364 |
+
- `hub_model_id`: None
|
365 |
+
- `hub_strategy`: every_save
|
366 |
+
- `hub_private_repo`: False
|
367 |
+
- `hub_always_push`: False
|
368 |
+
- `gradient_checkpointing`: False
|
369 |
+
- `gradient_checkpointing_kwargs`: None
|
370 |
+
- `include_inputs_for_metrics`: False
|
371 |
+
- `eval_do_concat_batches`: True
|
372 |
+
- `fp16_backend`: auto
|
373 |
+
- `push_to_hub_model_id`: None
|
374 |
+
- `push_to_hub_organization`: None
|
375 |
+
- `mp_parameters`:
|
376 |
+
- `auto_find_batch_size`: False
|
377 |
+
- `full_determinism`: False
|
378 |
+
- `torchdynamo`: None
|
379 |
+
- `ray_scope`: last
|
380 |
+
- `ddp_timeout`: 1800
|
381 |
+
- `torch_compile`: False
|
382 |
+
- `torch_compile_backend`: None
|
383 |
+
- `torch_compile_mode`: None
|
384 |
+
- `dispatch_batches`: None
|
385 |
+
- `split_batches`: None
|
386 |
+
- `include_tokens_per_second`: False
|
387 |
+
- `include_num_input_tokens_seen`: False
|
388 |
+
- `neftune_noise_alpha`: None
|
389 |
+
- `optim_target_modules`: None
|
390 |
+
- `batch_eval_metrics`: False
|
391 |
+
- `eval_on_start`: False
|
392 |
+
- `batch_sampler`: batch_sampler
|
393 |
+
- `multi_dataset_batch_sampler`: proportional
|
394 |
+
|
395 |
+
</details>
|
396 |
+
|
397 |
+
### Training Logs
|
398 |
+
| Epoch | Step | Training Loss | loss | pritamdeka/stsb-assamese-translated-dev_spearman_cosine | pritamdeka/stsb-assamese-translated-test_spearman_cosine |
|
399 |
+
|:------:|:----:|:-------------:|:------:|:-------------------------------------------------------:|:--------------------------------------------------------:|
|
400 |
+
| 0.2778 | 100 | 0.0316 | 0.0274 | 0.8415 | - |
|
401 |
+
| 0.5556 | 200 | 0.0306 | 0.0280 | 0.8392 | - |
|
402 |
+
| 0.8333 | 300 | 0.0282 | 0.0280 | 0.8462 | - |
|
403 |
+
| 1.1111 | 400 | 0.0208 | 0.0277 | 0.8482 | - |
|
404 |
+
| 1.3889 | 500 | 0.0148 | 0.0271 | 0.8494 | - |
|
405 |
+
| 1.6667 | 600 | 0.0136 | 0.0259 | 0.8503 | - |
|
406 |
+
| 1.9444 | 700 | 0.0137 | 0.0259 | 0.8525 | - |
|
407 |
+
| 2.2222 | 800 | 0.0089 | 0.0262 | 0.8519 | - |
|
408 |
+
| 2.5 | 900 | 0.0074 | 0.0255 | 0.8551 | - |
|
409 |
+
| 2.7778 | 1000 | 0.0071 | 0.0256 | 0.8544 | - |
|
410 |
+
| 3.0556 | 1100 | 0.0068 | 0.0258 | 0.8558 | - |
|
411 |
+
| 3.3333 | 1200 | 0.005 | 0.0253 | 0.8565 | - |
|
412 |
+
| 3.6111 | 1300 | 0.0046 | 0.0259 | 0.8547 | - |
|
413 |
+
| 3.8889 | 1400 | 0.0046 | 0.0257 | 0.8559 | - |
|
414 |
+
| 4.0 | 1440 | - | - | - | 0.8235 |
|
415 |
+
|
416 |
+
|
417 |
+
### Framework Versions
|
418 |
+
- Python: 3.10.12
|
419 |
+
- Sentence Transformers: 3.0.1
|
420 |
+
- Transformers: 4.42.4
|
421 |
+
- PyTorch: 2.3.1+cu121
|
422 |
+
- Accelerate: 0.32.1
|
423 |
+
- Datasets: 2.20.0
|
424 |
+
- Tokenizers: 0.19.1
|
425 |
+
|
426 |
+
## Citation
|
427 |
+
|
428 |
+
### BibTeX
|
429 |
+
|
430 |
+
#### Sentence Transformers
|
431 |
+
```bibtex
|
432 |
+
@inproceedings{reimers-2019-sentence-bert,
|
433 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
434 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
435 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
436 |
+
month = "11",
|
437 |
+
year = "2019",
|
438 |
+
publisher = "Association for Computational Linguistics",
|
439 |
+
url = "https://arxiv.org/abs/1908.10084",
|
440 |
+
}
|
441 |
+
```
|
442 |
+
|
443 |
+
<!--
|
444 |
+
## Glossary
|
445 |
+
|
446 |
+
*Clearly define terms in order to be accessible across audiences.*
|
447 |
+
-->
|
448 |
+
|
449 |
+
<!--
|
450 |
+
## Model Card Authors
|
451 |
+
|
452 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
453 |
+
-->
|
454 |
+
|
455 |
+
<!--
|
456 |
+
## Model Card Contact
|
457 |
+
|
458 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
459 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "output/training_stsbenchmark_pritamdeka-assamese-bert-nli-v2-2024-07-19_16-00-18/final",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"classifier_dropout": null,
|
8 |
+
"embedding_size": 768,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 768,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 3072,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 12,
|
19 |
+
"pad_token_id": 0,
|
20 |
+
"position_embedding_type": "absolute",
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.42.4",
|
23 |
+
"type_vocab_size": 2,
|
24 |
+
"use_cache": true,
|
25 |
+
"vocab_size": 197285
|
26 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.0.1",
|
4 |
+
"transformers": "4.42.4",
|
5 |
+
"pytorch": "2.3.1+cu121"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f20c3b9e456b166ff6711bc55b8b5f81544e8cdea9a9b575541ea33ee451e2f0
|
3 |
+
size 950247272
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"mask_token": {
|
10 |
+
"content": "[MASK]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
tokenizer.json
ADDED
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|
tokenizer_config.json
ADDED
@@ -0,0 +1,65 @@
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|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"100": {
|
12 |
+
"content": "[UNK]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"103": {
|
20 |
+
"content": "[MASK]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"104": {
|
28 |
+
"content": "[CLS]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"105": {
|
36 |
+
"content": "[SEP]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"clean_up_tokenization_spaces": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_basic_tokenize": true,
|
47 |
+
"do_lower_case": false,
|
48 |
+
"lowercase": false,
|
49 |
+
"mask_token": "[MASK]",
|
50 |
+
"max_length": 512,
|
51 |
+
"model_max_length": 512,
|
52 |
+
"never_split": null,
|
53 |
+
"pad_to_multiple_of": null,
|
54 |
+
"pad_token": "[PAD]",
|
55 |
+
"pad_token_type_id": 0,
|
56 |
+
"padding_side": "right",
|
57 |
+
"sep_token": "[SEP]",
|
58 |
+
"stride": 0,
|
59 |
+
"strip_accents": false,
|
60 |
+
"tokenize_chinese_chars": true,
|
61 |
+
"tokenizer_class": "BertTokenizer",
|
62 |
+
"truncation_side": "right",
|
63 |
+
"truncation_strategy": "longest_first",
|
64 |
+
"unk_token": "[UNK]"
|
65 |
+
}
|
vocab.txt
ADDED
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|
|