Add new SentenceTransformer model.
Browse files- README.md +124 -38
- config.json +3 -3
- config_sentence_transformers.json +5 -4
- model.safetensors +2 -2
- modules.json +14 -0
README.md
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should probably proofread and complete it, then remove this comment. -->
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# pre-UAE-Medical-Large-V1
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- total_train_batch_size: 64
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- total_eval_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 50
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- num_epochs: 1
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---
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base_model: WhereIsAI/pre-UAE-Medical-Large-V1
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datasets: []
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language: []
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library_name: sentence-transformers
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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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widget: []
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---
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# SentenceTransformer based on WhereIsAI/pre-UAE-Medical-Large-V1
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [WhereIsAI/pre-UAE-Medical-Large-V1](https://huggingface.co/WhereIsAI/pre-UAE-Medical-Large-V1). 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.
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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:** [WhereIsAI/pre-UAE-Medical-Large-V1](https://huggingface.co/WhereIsAI/pre-UAE-Medical-Large-V1) <!-- at revision c989d8965d489e9a6e873eabce06e6ef6f2a0188 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 1024 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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("WhereIsAI/pre-UAE-Medical-Large-V1")
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# Run inference
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sentences = [
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'The weather is lovely today.',
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"It's so sunny outside!",
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'He drove to the stadium.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 1024]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Framework Versions
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- Python: 3.10.12
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- Sentence Transformers: 3.0.1
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- Transformers: 4.42.3
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- PyTorch: 2.3.0+cu121
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- Accelerate: 0.30.1
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- Datasets: 2.19.1
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- Tokenizers: 0.19.1
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## Citation
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### BibTeX
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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<!--
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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<!--
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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-->
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config.json
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{
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"_name_or_path": "WhereIsAI/UAE-Large-V1",
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"architectures": [
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"
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "
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"transformers_version": "4.42.3",
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"type_vocab_size": 2,
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"use_cache": false,
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"_name_or_path": "WhereIsAI/pre-UAE-Medical-Large-V1",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float16",
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"transformers_version": "4.42.3",
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"type_vocab_size": 2,
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"use_cache": false,
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config_sentence_transformers.json
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"__version__": {
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"sentence_transformers": "
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"transformers": "4.
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"pytorch": "2.
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"prompts": {},
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"default_prompt_name": null
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"__version__": {
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"sentence_transformers": "3.0.1",
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"transformers": "4.42.3",
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"pytorch": "2.3.0+cu121"
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"prompts": {},
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"default_prompt_name": null,
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"similarity_fn_name": null
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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version https://git-lfs.github.com/spec/v1
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oid sha256:790f6e90679346a3155d57a008e8ac4d3efb79c43123f6573bbea0c8bad1cec2
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size 670328392
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modules.json
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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