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msgfrom96
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emotion_model

Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
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msgfrom96/xlm_emo_multi

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Files changed (5) hide show
  1. README.md +18 -15
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
  4. xlm_multi_emo/model.safetensors +1 -1
  5. xlm_multi_emo/training_args.bin +1 -1
README.md CHANGED
@@ -4,6 +4,8 @@ license: mit
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  base_model: xlm-roberta-large
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  tags:
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  - generated_from_trainer
 
 
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  model-index:
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  - name: emotion_model
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  results: []
@@ -16,9 +18,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2936
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- - Micro F1: 0.7177
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- - Macro F1: 0.5792
 
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
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- | 0.4725 | 1.0 | 143 | 0.3610 | 0.5857 | 0.3394 |
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- | 0.3219 | 2.0 | 286 | 0.3058 | 0.6957 | 0.5022 |
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- | 0.2892 | 3.0 | 429 | 0.3067 | 0.6814 | 0.4921 |
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- | 0.263 | 4.0 | 572 | 0.2915 | 0.7037 | 0.5397 |
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- | 0.2399 | 5.0 | 715 | 0.2911 | 0.7108 | 0.5625 |
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- | 0.2255 | 6.0 | 858 | 0.2922 | 0.7115 | 0.5833 |
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- | 0.2147 | 7.0 | 1001 | 0.2877 | 0.7115 | 0.5792 |
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- | 0.1989 | 8.0 | 1144 | 0.2922 | 0.7176 | 0.5882 |
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- | 0.1908 | 9.0 | 1287 | 0.2956 | 0.7162 | 0.5816 |
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- | 0.1851 | 10.0 | 1430 | 0.2970 | 0.7193 | 0.5984 |
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  ### Framework versions
 
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  base_model: xlm-roberta-large
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  tags:
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  - generated_from_trainer
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+ metrics:
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+ - accuracy
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  model-index:
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  - name: emotion_model
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  results: []
 
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  This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1691
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+ - Macro F1: 0.5721
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+ - Micro F1: 0.7014
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+ - Accuracy: 0.8780
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
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+ | 0.2428 | 1.0 | 143 | 0.2269 | 0.0016 | 0.0028 | 0.7811 |
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+ | 0.1979 | 2.0 | 286 | 0.1774 | 0.4377 | 0.6399 | 0.8642 |
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+ | 0.1712 | 3.0 | 429 | 0.1669 | 0.4939 | 0.6727 | 0.8729 |
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+ | 0.1571 | 4.0 | 572 | 0.1635 | 0.5474 | 0.6889 | 0.8768 |
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+ | 0.1426 | 5.0 | 715 | 0.1666 | 0.5658 | 0.6881 | 0.8737 |
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+ | 0.1335 | 6.0 | 858 | 0.1665 | 0.5824 | 0.6999 | 0.8750 |
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+ | 0.1236 | 7.0 | 1001 | 0.1682 | 0.5765 | 0.6940 | 0.8735 |
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+ | 0.1152 | 8.0 | 1144 | 0.1697 | 0.5747 | 0.6964 | 0.8752 |
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+ | 0.1104 | 9.0 | 1287 | 0.1732 | 0.5708 | 0.6930 | 0.8732 |
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+ | 0.1069 | 10.0 | 1430 | 0.1742 | 0.5814 | 0.6959 | 0.8738 |
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  ### Framework versions
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