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scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_d

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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  1. README.md +35 -14
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -23,10 +23,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.3333333333333333
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  - name: F1
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  type: f1
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- value: 0.16666666666666666
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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
@@ -36,9 +36,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1003
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- - Accuracy: 0.3333
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- - F1: 0.1667
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  ## Model description
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@@ -57,7 +57,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 53
@@ -67,14 +67,35 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 1.1049 | 1.09 | 500 | 1.0987 | 0.3333 | 0.1667 |
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- | 1.1026 | 2.17 | 1000 | 1.0989 | 0.3333 | 0.1667 |
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- | 1.1037 | 3.26 | 1500 | 1.1017 | 0.3333 | 0.1667 |
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- | 1.1029 | 4.35 | 2000 | 1.0994 | 0.3333 | 0.1667 |
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- | 1.1026 | 5.43 | 2500 | 1.0988 | 0.3333 | 0.1667 |
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- | 1.102 | 6.52 | 3000 | 1.1003 | 0.3333 | 0.1667 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6396604938271605
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  - name: F1
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  type: f1
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+ value: 0.6384456793550767
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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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.8506
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+ - Accuracy: 0.6397
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+ - F1: 0.6384
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 53
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | 0.9598 | 1.09 | 500 | 0.8321 | 0.6335 | 0.6229 |
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+ | 0.7983 | 2.17 | 1000 | 0.7922 | 0.6381 | 0.6278 |
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+ | 0.7031 | 3.26 | 1500 | 0.8300 | 0.6520 | 0.6468 |
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+ | 0.6192 | 4.35 | 2000 | 0.8659 | 0.6497 | 0.6443 |
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+ | 0.5472 | 5.43 | 2500 | 0.9646 | 0.6331 | 0.6343 |
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+ | 0.4664 | 6.52 | 3000 | 0.9555 | 0.6485 | 0.6455 |
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+ | 0.4025 | 7.61 | 3500 | 1.0121 | 0.6427 | 0.6405 |
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+ | 0.3568 | 8.7 | 4000 | 1.1016 | 0.6327 | 0.6324 |
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+ | 0.3069 | 9.78 | 4500 | 1.2521 | 0.6408 | 0.6400 |
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+ | 0.2701 | 10.87 | 5000 | 1.3727 | 0.6397 | 0.6372 |
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+ | 0.2398 | 11.96 | 5500 | 1.4539 | 0.6319 | 0.6334 |
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+ | 0.2004 | 13.04 | 6000 | 1.6097 | 0.6420 | 0.6376 |
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+ | 0.1864 | 14.13 | 6500 | 1.6302 | 0.6343 | 0.6349 |
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+ | 0.157 | 15.22 | 7000 | 1.7491 | 0.6381 | 0.6339 |
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+ | 0.1411 | 16.3 | 7500 | 1.8634 | 0.6400 | 0.6392 |
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+ | 0.1318 | 17.39 | 8000 | 2.0229 | 0.6277 | 0.6275 |
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+ | 0.1159 | 18.48 | 8500 | 2.0196 | 0.6385 | 0.6359 |
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+ | 0.1135 | 19.57 | 9000 | 2.1959 | 0.6377 | 0.6368 |
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+ | 0.1018 | 20.65 | 9500 | 2.3238 | 0.6323 | 0.6320 |
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+ | 0.0888 | 21.74 | 10000 | 2.3449 | 0.6339 | 0.6341 |
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+ | 0.0797 | 22.83 | 10500 | 2.4967 | 0.6354 | 0.6338 |
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+ | 0.0828 | 23.91 | 11000 | 2.5070 | 0.6358 | 0.6362 |
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+ | 0.0675 | 25.0 | 11500 | 2.5895 | 0.6381 | 0.6393 |
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+ | 0.067 | 26.09 | 12000 | 2.6730 | 0.6370 | 0.6372 |
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+ | 0.0566 | 27.17 | 12500 | 2.7454 | 0.6377 | 0.6386 |
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+ | 0.0571 | 28.26 | 13000 | 2.7673 | 0.6420 | 0.6413 |
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+ | 0.048 | 29.35 | 13500 | 2.8506 | 0.6397 | 0.6384 |
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  ### Framework versions
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