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End of training

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Files changed (5) hide show
  1. README.md +15 -17
  2. config.json +7 -7
  3. pytorch_model.bin +2 -2
  4. tokenizer_config.json +42 -0
  5. training_args.bin +2 -2
README.md CHANGED
@@ -23,12 +23,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value:
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- f1: 0.8172545518133599
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  - name: Accuracy
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  type: accuracy
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- value:
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- accuracy: 0.8328748280605227
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -38,9 +36,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7236
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- - F1: {'f1': 0.8172545518133599}
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- - Accuracy: {'accuracy': 0.8328748280605227}
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  ## Model description
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@@ -69,17 +67,17 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------------------------:|:--------------------------------:|
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- | 0.3941 | 0.94 | 100 | 0.4098 | {'f1': 0.7962755487135231} | {'accuracy': 0.828060522696011} |
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- | 0.1921 | 1.89 | 200 | 0.5360 | {'f1': 0.8094154455783058} | {'accuracy': 0.8321870701513068} |
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- | 0.0873 | 2.83 | 300 | 0.7086 | {'f1': 0.8146311198809535} | {'accuracy': 0.8301237964236589} |
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- | 0.0404 | 3.77 | 400 | 0.7236 | {'f1': 0.8172545518133599} | {'accuracy': 0.8328748280605227} |
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  ### Framework versions
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- - Transformers 4.33.2
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- - Pytorch 2.0.1+cu118
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- - Datasets 2.14.5
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- - Tokenizers 0.13.3
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.8199689183645938
 
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8370013755158184
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6190
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+ - F1: 0.8200
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+ - Accuracy: 0.8370
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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+ | 0.6472 | 0.94 | 100 | 0.5388 | 0.7457 | 0.7558 |
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+ | 0.2832 | 1.89 | 200 | 0.4992 | 0.8072 | 0.8177 |
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+ | 0.1671 | 2.83 | 300 | 0.5158 | 0.8190 | 0.8377 |
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+ | 0.0834 | 3.77 | 400 | 0.6190 | 0.8200 | 0.8370 |
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  ### Framework versions
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+ - Transformers 4.34.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
config.json CHANGED
@@ -10,16 +10,16 @@
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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- "0": "negative",
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- "1": "neutral",
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  },
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  "initializer_range": 0.02,
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  },
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  "position_embedding_type": "absolute",
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  "problem_type": "single_label_classification",
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  "torch_dtype": "float32",
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- "transformers_version": "4.33.2",
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  "type_vocab_size": 2,
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  "use_cache": true,
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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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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  "position_embedding_type": "absolute",
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  "problem_type": "single_label_classification",
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  "torch_dtype": "float32",
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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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