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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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tags: |
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- regression |
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- generated_from_trainer |
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model-index: |
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- name: stsb-all-MiniLM-L6-v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# stsb-all-MiniLM-L6-v2 |
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This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 on the Semantic Textual Similarity Benchmark (STS-B) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0307 |
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- Pearson: 0.8287 |
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## Model description |
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This model is fine-tuned from the pre-trained sentence-transformers/all-MiniLM-L6-v2 on the Semantic Textual Similarity Benchmark (STS-B) dataset. It is designed to compute similarity scores between pairs of sentences, returning a continuous score between 0 and 1, where 1 represents maximum semantic similarity. |
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The model generates embeddings for input sentences and can be used for tasks such as text similarity, sentence clustering, or semantic search. |
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## Training and evaluation data |
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The model was trained on the STS-B dataset using the following splits: |
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Train set: 5,749 examples |
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Validation set: 1,500 examples |
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Test set: 1,379 examples |
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Each example consists of two sentences and a similarity score (from 0 to 1) indicating their semantic closeness. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Pearson | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| No log | 1.0 | 360 | 0.0354 | 0.7935 | |
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| 0.0483 | 2.0 | 720 | 0.0391 | 0.8124 | |
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| 0.021 | 3.0 | 1080 | 0.0332 | 0.8206 | |
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| 0.021 | 4.0 | 1440 | 0.0296 | 0.8296 | |
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| 0.0155 | 5.0 | 1800 | 0.0307 | 0.8287 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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