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1_Pooling/config.json +3 -1
README.md +8 -9
config.json +1 -1
eval/.ipynb_checkpoints/Information-Retrieval_evaluation_results-checkpoint.csv +1 -127
eval/Information-Retrieval_evaluation_results.csv +0 -0
model.safetensors +1 -1
tokenizer_config.json +7 -0

1_Pooling/config.json CHANGED Viewed

@@ -3,5 +3,7 @@
   "pooling_mode_cls_token": true,
   "pooling_mode_mean_tokens": false,
   "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false
 }

   "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
 }

README.md CHANGED Viewed

@@ -1,9 +1,11 @@
 ---
-pipeline_tag: feature-extraction
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 ---
 # {MODEL_NAME}
@@ -45,22 +47,19 @@ The model was trained with the parameters:
 **DataLoader**:
-`torch.utils.data.dataloader.DataLoader` of length 3898 with parameters:
 ```
 {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 **Loss**:
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
 Parameters of the fit()-Method:
 ```
 {
-    "epochs": 3,
     "evaluation_steps": 50,
     "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
     "max_grad_norm": 1,
@@ -70,7 +69,7 @@ Parameters of the fit()-Method:
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
-    "warmup_steps": 1169,
     "weight_decay": 0.01
 }
 ```
@@ -80,7 +79,7 @@ Parameters of the fit()-Method:
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
-  (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})
   (2): Normalize()
 )
 ```

 ---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 ---
 # {MODEL_NAME}
 **DataLoader**:
+`torch.utils.data.dataloader.DataLoader` of length 22604 with parameters:
 ```
 {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 **Loss**:
+`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
 Parameters of the fit()-Method:
 ```
 {
+    "epochs": 2,
     "evaluation_steps": 50,
     "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
     "max_grad_norm": 1,
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
+    "warmup_steps": 4520,
     "weight_decay": 0.01
 }
 ```
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
+  (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})
   (2): Normalize()
 )
 ```

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/root/.cache/torch/sentence_transformers/BAAI_bge-large-en-v1.5/",
   "architectures": [
     "BertModel"
   ],

 {
+  "_name_or_path": "exp_finetune",
   "architectures": [
     "BertModel"
   ],

eval/.ipynb_checkpoints/Information-Retrieval_evaluation_results-checkpoint.csv CHANGED Viewed

@@ -1,128 +1,2 @@
 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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