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Browse files- README.md +2 -2
- config.json +15 -11
- model.safetensors +2 -2
- optimizer.pt +2 -2
- rng_state.pth +2 -2
- scheduler.pt +1 -1
- sentence_bert_config.json +1 -1
- special_tokens_map.json +6 -20
- tokenizer.json +2 -2
- tokenizer_config.json +22 -20
- trainer_state.json +0 -0
- training_args.bin +1 -1
- vocab.txt +0 -0
README.md
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@@ -16,7 +16,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps
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### Model Description
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- **Model Type:** Sentence Transformer
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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-
- **Maximum Sequence Length:**
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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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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length':
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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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(2): Normalize()
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)
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### Model Description
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- **Model Type:** Sentence Transformer
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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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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```
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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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(2): Normalize()
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)
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config.json
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@@ -1,28 +1,32 @@
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{
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"_name_or_path": "/root/FlagEmbedding/model/checkpoint-
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"architectures": [
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"
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],
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"attention_probs_dropout_prob": 0.1,
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"
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.45.2",
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"type_vocab_size":
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"use_cache": true,
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"vocab_size":
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{
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"_name_or_path": "/root/FlagEmbedding/model-bgev1.5/checkpoint-23000",
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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": 1024,
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"id2label": {
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"0": "LABEL_0"
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"initializer_range": 0.02,
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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": 16,
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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": "float32",
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"transformers_version": "4.45.2",
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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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}
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model.safetensors
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optimizer.pt
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rng_state.pth
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scheduler.pt
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sentence_bert_config.json
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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trainer_state.json
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training_args.bin
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vocab.txt
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