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Browse files- models/bge-small_finetuned/1_Pooling/config.json +10 -0
- models/bge-small_finetuned/README.md +92 -0
- models/bge-small_finetuned/config.json +31 -0
- models/bge-small_finetuned/config_sentence_transformers.json +9 -0
- models/bge-small_finetuned/eval/Information-Retrieval_evaluation_results.csv +81 -0
- models/bge-small_finetuned/modules.json +20 -0
- models/bge-small_finetuned/sentence_bert_config.json +4 -0
- models/bge-small_finetuned/special_tokens_map.json +37 -0
- models/bge-small_finetuned/tokenizer.json +0 -0
- models/bge-small_finetuned/tokenizer_config.json +57 -0
- models/bge-small_finetuned/vocab.txt +0 -0
models/bge-small_finetuned/1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": true,
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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": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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models/bge-small_finetuned/README.md
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---
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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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- feature-extraction
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- sentence-similarity
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---
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# {MODEL_NAME}
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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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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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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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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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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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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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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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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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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 66 with parameters:
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```
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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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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Parameters of the fit()-Method:
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```
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{
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"epochs": 40,
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"evaluation_steps": 50,
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"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.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": 264,
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"weight_decay": 0.01
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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': 512, 'do_lower_case': True}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 384, '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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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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models/bge-small_finetuned/config.json
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{
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"_name_or_path": "BAAI/bge-small-en",
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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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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"label2id": {
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"LABEL_0": 0
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},
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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": 12,
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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": "4.39.3",
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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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models/bge-small_finetuned/config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.2.2",
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"transformers": "4.28.1",
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"pytorch": "1.13.0+cu117"
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},
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"prompts": {},
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"default_prompt_name": null
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}
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models/bge-small_finetuned/eval/Information-Retrieval_evaluation_results.csv
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epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100,dot_score-Accuracy@1,dot_score-Accuracy@3,dot_score-Accuracy@5,dot_score-Accuracy@10,dot_score-Precision@1,dot_score-Recall@1,dot_score-Precision@3,dot_score-Recall@3,dot_score-Precision@5,dot_score-Recall@5,dot_score-Precision@10,dot_score-Recall@10,dot_score-MRR@10,dot_score-NDCG@10,dot_score-MAP@100
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76 |
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37,50,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5952204585537918,0.6420566979067942,0.6056742803263043,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5952204585537918,0.6420566979067942,0.6056742803263043
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77 |
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37,-1,0.5288888888888889,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5288888888888889,0.5288888888888889,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.597363315696649,0.6436175947507096,0.6077897226134856,0.5288888888888889,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5288888888888889,0.5288888888888889,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.597363315696649,0.6436175947507096,0.6077897226134856
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78 |
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38,50,0.5288888888888889,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5288888888888889,0.5288888888888889,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.597363315696649,0.6436175947507096,0.6078072929491134,0.5288888888888889,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5288888888888889,0.5288888888888889,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.597363315696649,0.6436175947507096,0.6078072929491134
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79 |
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38,-1,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5954426807760141,0.6422514701327446,0.6059070675355129,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5954426807760141,0.6422514701327446,0.6059070675355129
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80 |
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39,50,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5954426807760141,0.6422514701327446,0.6059095284682064,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5954426807760141,0.6422514701327446,0.6059095284682064
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81 |
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39,-1,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5954426807760141,0.6422514701327446,0.6059095284682064,0.5244444444444445,0.6222222222222222,0.6755555555555556,0.7955555555555556,0.5244444444444445,0.5244444444444445,0.20740740740740737,0.6222222222222222,0.1351111111111111,0.6755555555555556,0.07955555555555555,0.7955555555555556,0.5954426807760141,0.6422514701327446,0.6059095284682064
|
models/bge-small_finetuned/modules.json
ADDED
@@ -0,0 +1,20 @@
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|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
models/bge-small_finetuned/sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
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|
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|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": true
|
4 |
+
}
|
models/bge-small_finetuned/special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
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|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
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"content": "[CLS]",
|
4 |
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"lstrip": false,
|
5 |
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"normalized": false,
|
6 |
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"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"mask_token": {
|
10 |
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"content": "[MASK]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
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"content": "[UNK]",
|
32 |
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"lstrip": false,
|
33 |
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"normalized": false,
|
34 |
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"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
models/bge-small_finetuned/tokenizer.json
ADDED
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models/bge-small_finetuned/tokenizer_config.json
ADDED
@@ -0,0 +1,57 @@
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|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
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|
7 |
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"rstrip": false,
|
8 |
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"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"100": {
|
12 |
+
"content": "[UNK]",
|
13 |
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|
14 |
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|
15 |
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"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"101": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"102": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"103": {
|
36 |
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"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
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"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"clean_up_tokenization_spaces": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_basic_tokenize": true,
|
47 |
+
"do_lower_case": true,
|
48 |
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"mask_token": "[MASK]",
|
49 |
+
"model_max_length": 512,
|
50 |
+
"never_split": null,
|
51 |
+
"pad_token": "[PAD]",
|
52 |
+
"sep_token": "[SEP]",
|
53 |
+
"strip_accents": null,
|
54 |
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"tokenize_chinese_chars": true,
|
55 |
+
"tokenizer_class": "BertTokenizer",
|
56 |
+
"unk_token": "[UNK]"
|
57 |
+
}
|
models/bge-small_finetuned/vocab.txt
ADDED
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|
|