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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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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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+ }
README.md ADDED
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+ ---
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - feature-extraction
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+ - sentence-similarity
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+ ---
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+
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+ # {MODEL_NAME}
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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+ model = SentenceTransformer('{MODEL_NAME}')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+
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+
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+ ## Evaluation Results
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+
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+ <!--- Describe how your model was evaluated -->
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+
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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+
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+
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+ ## Training
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+ The model was trained with the parameters:
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+
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+ **DataLoader**:
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+
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+ `torch.utils.data.dataloader.DataLoader` of length 312500 with parameters:
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+ ```
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+ {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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+ ```
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+
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+ **Loss**:
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+
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+ `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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+
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+ Parameters of the fit()-Method:
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+ ```
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+ {
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+ "epochs": 1,
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+ "evaluation_steps": 1000,
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+ "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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+ "max_grad_norm": 1,
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+ "optimizer_class": "<class 'transformers.optimization.AdamW'>",
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+ "optimizer_params": {
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+ "lr": 2e-05
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+ },
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+ "scheduler": "WarmupLinear",
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+ "steps_per_epoch": null,
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+ "warmup_steps": 100,
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+ "weight_decay": 0.01
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+ }
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+ ```
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+
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+
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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': 256, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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+ (2): Normalize()
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+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
config.json ADDED
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+ {
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+ "_name_or_path": "/home/hephaestus/.cache/torch/sentence_transformers/sentence-transformers_all-MiniLM-L6-v2/",
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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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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 384,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1536,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.18.0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.0.0",
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+ "transformers": "4.6.1",
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+ "pytorch": "1.8.1"
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+ }
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+ }
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