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haryoaw
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scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a

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 -16
  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: 0.6180555555555556
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  - name: F1
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  type: f1
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- value: 0.616361106308878
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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: 1.6171
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- - Accuracy: 0.6181
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- - F1: 0.6164
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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: 1234
@@ -67,16 +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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- | 0.9992 | 1.09 | 500 | 0.9022 | 0.5895 | 0.5790 |
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- | 0.8353 | 2.17 | 1000 | 0.8484 | 0.6169 | 0.6146 |
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- | 0.7029 | 3.26 | 1500 | 0.9391 | 0.6312 | 0.6286 |
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- | 0.5653 | 4.35 | 2000 | 1.0629 | 0.6157 | 0.6032 |
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- | 0.4622 | 5.43 | 2500 | 1.1849 | 0.6169 | 0.6091 |
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- | 0.3614 | 6.52 | 3000 | 1.2831 | 0.6184 | 0.6190 |
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- | 0.284 | 7.61 | 3500 | 1.3589 | 0.6177 | 0.6196 |
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- | 0.2384 | 8.7 | 4000 | 1.6171 | 0.6181 | 0.6164 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.6439043209876543
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  - name: F1
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  type: f1
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+ value: 0.6443757148090576
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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: 2.6822
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+ - Accuracy: 0.6439
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+ - F1: 0.6444
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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: 1234
 
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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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+ | 0.9412 | 1.09 | 500 | 0.8062 | 0.6389 | 0.6335 |
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+ | 0.7943 | 2.17 | 1000 | 0.8448 | 0.6451 | 0.6394 |
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+ | 0.7026 | 3.26 | 1500 | 0.8509 | 0.6497 | 0.6438 |
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+ | 0.6019 | 4.35 | 2000 | 0.8999 | 0.6478 | 0.6468 |
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+ | 0.5379 | 5.43 | 2500 | 0.9424 | 0.6312 | 0.6222 |
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+ | 0.4635 | 6.52 | 3000 | 1.0401 | 0.6431 | 0.6439 |
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+ | 0.3985 | 7.61 | 3500 | 1.0584 | 0.6397 | 0.6390 |
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+ | 0.3506 | 8.7 | 4000 | 1.1607 | 0.6443 | 0.6432 |
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+ | 0.3105 | 9.78 | 4500 | 1.1806 | 0.6408 | 0.6423 |
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+ | 0.2712 | 10.87 | 5000 | 1.3112 | 0.6316 | 0.6304 |
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+ | 0.2361 | 11.96 | 5500 | 1.3772 | 0.6466 | 0.6454 |
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+ | 0.2111 | 13.04 | 6000 | 1.4492 | 0.6385 | 0.6396 |
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+ | 0.1885 | 14.13 | 6500 | 1.6604 | 0.6335 | 0.6347 |
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+ | 0.1658 | 15.22 | 7000 | 1.7153 | 0.6358 | 0.6353 |
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+ | 0.1501 | 16.3 | 7500 | 1.7849 | 0.6412 | 0.6427 |
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+ | 0.135 | 17.39 | 8000 | 1.9749 | 0.6416 | 0.6394 |
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+ | 0.1217 | 18.48 | 8500 | 2.0530 | 0.6439 | 0.6431 |
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+ | 0.1112 | 19.57 | 9000 | 2.1378 | 0.6439 | 0.6448 |
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+ | 0.1018 | 20.65 | 9500 | 2.2656 | 0.6393 | 0.6390 |
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+ | 0.0885 | 21.74 | 10000 | 2.3568 | 0.6431 | 0.6438 |
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+ | 0.0897 | 22.83 | 10500 | 2.3852 | 0.6435 | 0.6446 |
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+ | 0.0854 | 23.91 | 11000 | 2.4019 | 0.6327 | 0.6329 |
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+ | 0.0734 | 25.0 | 11500 | 2.5260 | 0.6331 | 0.6333 |
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+ | 0.067 | 26.09 | 12000 | 2.5368 | 0.6470 | 0.6465 |
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+ | 0.0546 | 27.17 | 12500 | 2.6255 | 0.6431 | 0.6441 |
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+ | 0.0581 | 28.26 | 13000 | 2.6467 | 0.6458 | 0.6456 |
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+ | 0.0564 | 29.35 | 13500 | 2.6822 | 0.6439 | 0.6444 |
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  ### Framework versions
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