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scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_

Transformers
PyTorch
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +52 -52
  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: 0.5837191358024691
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  - name: F1
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  type: f1
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- value: 0.5817965608648601
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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: 4.2157
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- - Accuracy: 0.5837
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- - F1: 0.5818
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  ## Model description
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@@ -60,7 +60,7 @@ 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: 11423
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - num_epochs: 50
@@ -69,52 +69,52 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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- | 1.036 | 1.09 | 500 | 0.9628 | 0.5490 | 0.5319 |
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- | 0.8915 | 2.17 | 1000 | 0.9520 | 0.5818 | 0.5603 |
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- | 0.7492 | 3.26 | 1500 | 0.9273 | 0.5999 | 0.5963 |
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- | 0.6188 | 4.35 | 2000 | 1.0573 | 0.5891 | 0.5893 |
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- | 0.4757 | 5.43 | 2500 | 1.3072 | 0.5876 | 0.5833 |
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- | 0.3598 | 6.52 | 3000 | 1.4695 | 0.5702 | 0.5670 |
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- | 0.2775 | 7.61 | 3500 | 1.4816 | 0.5772 | 0.5715 |
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- | 0.2146 | 8.7 | 4000 | 1.6045 | 0.5737 | 0.5691 |
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- | 0.1741 | 9.78 | 4500 | 1.9439 | 0.5768 | 0.5730 |
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- | 0.156 | 10.87 | 5000 | 1.9573 | 0.5783 | 0.5744 |
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- | 0.1317 | 11.96 | 5500 | 2.4284 | 0.5683 | 0.5629 |
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- | 0.1063 | 13.04 | 6000 | 2.4312 | 0.5737 | 0.5707 |
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- | 0.0941 | 14.13 | 6500 | 2.5312 | 0.5760 | 0.5770 |
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- | 0.088 | 15.22 | 7000 | 2.9152 | 0.5694 | 0.5709 |
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- | 0.0761 | 16.3 | 7500 | 2.8682 | 0.5745 | 0.5745 |
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- | 0.0665 | 17.39 | 8000 | 3.0763 | 0.5745 | 0.5738 |
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- | 0.0603 | 18.48 | 8500 | 2.9069 | 0.5683 | 0.5674 |
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- | 0.0602 | 19.57 | 9000 | 2.9868 | 0.5691 | 0.5609 |
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- | 0.0475 | 20.65 | 9500 | 3.1230 | 0.5660 | 0.5645 |
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- | 0.0443 | 21.74 | 10000 | 3.2337 | 0.5729 | 0.5705 |
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- | 0.0437 | 22.83 | 10500 | 3.1570 | 0.5687 | 0.5672 |
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- | 0.0437 | 23.91 | 11000 | 3.1459 | 0.5752 | 0.5716 |
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- | 0.0322 | 25.0 | 11500 | 3.4992 | 0.5864 | 0.5842 |
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- | 0.0323 | 26.09 | 12000 | 3.4681 | 0.5745 | 0.5720 |
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- | 0.0292 | 27.17 | 12500 | 3.6051 | 0.5710 | 0.5672 |
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- | 0.026 | 28.26 | 13000 | 3.6130 | 0.5772 | 0.5768 |
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- | 0.0253 | 29.35 | 13500 | 3.7143 | 0.5795 | 0.5809 |
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- | 0.0242 | 30.43 | 14000 | 3.7550 | 0.5764 | 0.5709 |
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- | 0.0175 | 31.52 | 14500 | 3.6952 | 0.5849 | 0.5846 |
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- | 0.0202 | 32.61 | 15000 | 3.7597 | 0.5826 | 0.5816 |
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- | 0.0191 | 33.7 | 15500 | 3.7410 | 0.5799 | 0.5765 |
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- | 0.0182 | 34.78 | 16000 | 3.5524 | 0.5837 | 0.5832 |
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- | 0.0138 | 35.87 | 16500 | 3.7785 | 0.5860 | 0.5834 |
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- | 0.0138 | 36.96 | 17000 | 3.7310 | 0.5868 | 0.5872 |
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- | 0.0104 | 38.04 | 17500 | 3.8527 | 0.5849 | 0.5795 |
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- | 0.0103 | 39.13 | 18000 | 3.7762 | 0.5895 | 0.5871 |
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- | 0.0112 | 40.22 | 18500 | 3.7746 | 0.5930 | 0.5893 |
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- | 0.0108 | 41.3 | 19000 | 3.9045 | 0.5856 | 0.5852 |
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- | 0.0073 | 42.39 | 19500 | 4.0588 | 0.5910 | 0.5887 |
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- | 0.0061 | 43.48 | 20000 | 4.1100 | 0.5876 | 0.5844 |
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- | 0.0065 | 44.57 | 20500 | 4.1311 | 0.5849 | 0.5848 |
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- | 0.0057 | 45.65 | 21000 | 4.0796 | 0.5829 | 0.5809 |
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- | 0.0048 | 46.74 | 21500 | 4.1156 | 0.5806 | 0.5782 |
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- | 0.0036 | 47.83 | 22000 | 4.1293 | 0.5837 | 0.5826 |
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- | 0.0029 | 48.91 | 22500 | 4.1840 | 0.5806 | 0.5794 |
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- | 0.0025 | 50.0 | 23000 | 4.2157 | 0.5837 | 0.5818 |
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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: 0.589891975308642
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  - name: F1
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  type: f1
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+ value: 0.588413122388427
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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: 4.1967
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+ - Accuracy: 0.5899
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+ - F1: 0.5884
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  ## Model description
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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: 11213
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - num_epochs: 50
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | 1.0743 | 1.09 | 500 | 1.0031 | 0.5066 | 0.5042 |
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+ | 0.9346 | 2.17 | 1000 | 0.9437 | 0.5656 | 0.5679 |
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+ | 0.8123 | 3.26 | 1500 | 0.9108 | 0.5949 | 0.5905 |
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+ | 0.6842 | 4.35 | 2000 | 1.1082 | 0.5756 | 0.5661 |
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+ | 0.5603 | 5.43 | 2500 | 1.1812 | 0.5907 | 0.5828 |
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+ | 0.4284 | 6.52 | 3000 | 1.3230 | 0.5895 | 0.5870 |
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+ | 0.3295 | 7.61 | 3500 | 1.4855 | 0.5637 | 0.5638 |
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+ | 0.2589 | 8.7 | 4000 | 1.5869 | 0.5837 | 0.5784 |
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+ | 0.2035 | 9.78 | 4500 | 1.8098 | 0.5826 | 0.5776 |
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+ | 0.1755 | 10.87 | 5000 | 1.7393 | 0.5887 | 0.5856 |
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+ | 0.1497 | 11.96 | 5500 | 2.1213 | 0.5887 | 0.5828 |
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+ | 0.13 | 13.04 | 6000 | 2.2126 | 0.5833 | 0.5827 |
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+ | 0.1151 | 14.13 | 6500 | 2.2685 | 0.5818 | 0.5811 |
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+ | 0.1028 | 15.22 | 7000 | 2.5633 | 0.5826 | 0.5827 |
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+ | 0.0962 | 16.3 | 7500 | 2.4350 | 0.5795 | 0.5770 |
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+ | 0.0804 | 17.39 | 8000 | 2.6830 | 0.5806 | 0.5752 |
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+ | 0.0781 | 18.48 | 8500 | 2.6389 | 0.5818 | 0.5811 |
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+ | 0.0677 | 19.57 | 9000 | 2.6490 | 0.5806 | 0.5788 |
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+ | 0.0593 | 20.65 | 9500 | 2.9908 | 0.5768 | 0.5732 |
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+ | 0.0578 | 21.74 | 10000 | 2.9127 | 0.5845 | 0.5828 |
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+ | 0.0493 | 22.83 | 10500 | 3.0101 | 0.5802 | 0.5744 |
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+ | 0.0455 | 23.91 | 11000 | 2.9419 | 0.5795 | 0.5779 |
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+ | 0.0351 | 25.0 | 11500 | 3.2339 | 0.5752 | 0.5742 |
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+ | 0.0369 | 26.09 | 12000 | 3.2997 | 0.5899 | 0.5818 |
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+ | 0.0291 | 27.17 | 12500 | 3.5819 | 0.5833 | 0.5804 |
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+ | 0.0281 | 28.26 | 13000 | 3.4498 | 0.5795 | 0.5798 |
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+ | 0.0258 | 29.35 | 13500 | 3.5006 | 0.5768 | 0.5768 |
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+ | 0.027 | 30.43 | 14000 | 3.4740 | 0.5849 | 0.5832 |
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+ | 0.0218 | 31.52 | 14500 | 3.2293 | 0.5918 | 0.5907 |
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+ | 0.0227 | 32.61 | 15000 | 3.4840 | 0.5876 | 0.5861 |
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+ | 0.0212 | 33.7 | 15500 | 3.2922 | 0.5845 | 0.5841 |
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+ | 0.0119 | 34.78 | 16000 | 3.9035 | 0.5729 | 0.5744 |
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+ | 0.019 | 35.87 | 16500 | 3.5470 | 0.5795 | 0.5781 |
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+ | 0.0146 | 36.96 | 17000 | 3.7651 | 0.5795 | 0.5772 |
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+ | 0.0144 | 38.04 | 17500 | 3.7248 | 0.5829 | 0.5787 |
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+ | 0.0077 | 39.13 | 18000 | 4.1509 | 0.5806 | 0.5754 |
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+ | 0.0097 | 40.22 | 18500 | 3.8829 | 0.5829 | 0.5796 |
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+ | 0.0092 | 41.3 | 19000 | 3.8987 | 0.5853 | 0.5842 |
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+ | 0.0087 | 42.39 | 19500 | 3.8544 | 0.5899 | 0.5882 |
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+ | 0.0083 | 43.48 | 20000 | 3.9211 | 0.5895 | 0.5855 |
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+ | 0.006 | 44.57 | 20500 | 3.9856 | 0.5868 | 0.5856 |
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+ | 0.0062 | 45.65 | 21000 | 4.0873 | 0.5891 | 0.5872 |
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+ | 0.0027 | 46.74 | 21500 | 4.1639 | 0.5891 | 0.5888 |
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+ | 0.0052 | 47.83 | 22000 | 4.1754 | 0.5914 | 0.5893 |
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+ | 0.0031 | 48.91 | 22500 | 4.1914 | 0.5887 | 0.5879 |
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+ | 0.0035 | 50.0 | 23000 | 4.1967 | 0.5899 | 0.5884 |
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  ### Framework versions
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