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SGPT-1.3B-weightedmean-nli

Sentence Similarity
sentence-transformers
PyTorch
gpt_neo
feature-extraction
Model card Files Community
Muennighoff commited on
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Add SGPT-1.3B-weightedmean-nli

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Files changed (14) hide show
  1. 1_Pooling/config.json +9 -0
  2. README.md +89 -0
  3. config.json +74 -0
  4. config_sentence_transformers.json +7 -0
  5. eval/similarity_evaluation_sts-dev_results.csv +89 -0
  6. merges.txt +0 -0
  7. modules.json +14 -0
  8. pytorch_model.bin +3 -0
  9. sentence_bert_config.json +4 -0
  10. similarity_evaluation_sts-test_results.csv +9 -0
  11. special_tokens_map.json +1 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +1 -0
  14. vocab.json +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 2048,
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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
3
+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
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+ - sentence-similarity
7
+ ---
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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 2048 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"]
28
+
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+ model = SentenceTransformer('{MODEL_NAME}')
30
+ embeddings = model.encode(sentences)
31
+ 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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+
38
+ <!--- Describe how your model was evaluated -->
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+
40
+ 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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+
46
+ **DataLoader**:
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+
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+ `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 93941 with parameters:
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+ ```
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+ {'batch_size': 6}
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+ ```
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+
53
+ **Loss**:
54
+
55
+ `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
56
+ ```
57
+ {'scale': 20.0, 'similarity_fct': 'cos_sim'}
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+ ```
59
+
60
+ Parameters of the fit()-Method:
61
+ ```
62
+ {
63
+ "epochs": 1,
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+ "evaluation_steps": 9394,
65
+ "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": 1e-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": 9395,
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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
80
+ ```
81
+ SentenceTransformer(
82
+ (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel
83
+ (1): Pooling({'word_embedding_dimension': 2048, '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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+
87
+ ## Citing & Authors
88
+
89
+ <!--- Describe where people can find more information -->
config.json ADDED
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+ {
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+ "_name_or_path": "EleutherAI/gpt-neo-1.3B",
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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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+ "attention_dropout": 0,
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+ "attention_layers": [
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+ "global",
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+ "local",
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+ "global",
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+ ],
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+ "attention_types": [
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+ [
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+ [
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+ "global",
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+ "local"
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+ ],
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+ 12
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+ ]
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+ ],
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+ "bos_token_id": 50256,
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+ "embed_dropout": 0,
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+ "eos_token_id": 50256,
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+ "gradient_checkpointing": false,
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+ "hidden_size": 2048,
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+ "initializer_range": 0.02,
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+ "intermediate_size": null,
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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": 16,
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+ "num_layers": 24,
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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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+ "max_length": 50,
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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": 50257,
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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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+ }
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