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Add SGPT-2.7B-weightedmean-msmarco-specb-bitfit

Browse files
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 2560,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": false,
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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": true,
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+ "pooling_mode_lasttoken": 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 2560 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 124796 with parameters:
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+ ```
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+ {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', '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.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
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+ ```
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+ {'scale': 20.0, 'similarity_fct': 'cos_sim'}
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+ ```
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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": 10,
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+ "evaluation_steps": 0,
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+ "evaluator": "NoneType",
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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": 7.5e-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": 1000,
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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': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
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+ (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
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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 -->
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config.json ADDED
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+ {
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+ "_name_or_path": "EleutherAI/gpt-neo-2.7B",
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+ "activation_function": "gelu_new",
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+ "architectures": [
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+ "GPTNeoModel"
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+ ],
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+ "layer_norm_epsilon": 1e-05,
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+ "max_position_embeddings": 2048,
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+ "model_type": "gpt_neo",
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+ "num_heads": 20,
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+ "num_layers": 32,
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+ "resid_dropout": 0,
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+ "summary_activation": null,
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+ "summary_first_dropout": 0.1,
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+ "summary_proj_to_labels": true,
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+ "summary_type": "cls_index",
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+ "summary_use_proj": true,
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+ "task_specific_params": {
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+ "text-generation": {
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+ "do_sample": true,
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+ "temperature": 0.9
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+ }
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+ },
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+ "tokenizer_class": "GPT2Tokenizer",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.11.3",
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+ "use_cache": true,
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+ "vocab_size": 50259,
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+ "window_size": 256
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.1.0",
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+ "transformers": "4.11.3",
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+ "pytorch": "1.10.1"
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+ }
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+ }
merges.txt ADDED
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modules.json ADDED
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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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sentence_bert_config.json ADDED
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+ {
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+ "max_seq_length": 300,
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+ "do_lower_case": false
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+ }
special_tokens_map.json ADDED
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+ {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}
tokenizer.json ADDED
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tokenizer_config.json ADDED
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vocab.json ADDED
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