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scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_gamma

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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +109 -0
  2. config.json +39 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - tweet_sentiment_multilingual
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_gamma
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+ results: []
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+ ---
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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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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_gamma
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+
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+ This model is a fine-tuned version of [haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a](https://huggingface.co/haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a) on the tweet_sentiment_multilingual dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.5245
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+ - Accuracy: 0.5517
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+ - F1: 0.5524
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 88458
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | 4.8673 | 1.09 | 500 | 4.1044 | 0.4356 | 0.4381 |
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+ | 4.0355 | 2.17 | 1000 | 3.8162 | 0.5004 | 0.4829 |
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+ | 3.4812 | 3.26 | 1500 | 3.3484 | 0.5312 | 0.5299 |
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+ | 3.1323 | 4.35 | 2000 | 3.3401 | 0.5502 | 0.5500 |
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+ | 2.7632 | 5.43 | 2500 | 3.5126 | 0.5471 | 0.5441 |
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+ | 2.5101 | 6.52 | 3000 | 3.5161 | 0.5444 | 0.5412 |
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+ | 2.3266 | 7.61 | 3500 | 3.6769 | 0.5367 | 0.5263 |
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+ | 2.1096 | 8.7 | 4000 | 3.6299 | 0.5513 | 0.5501 |
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+ | 1.972 | 9.78 | 4500 | 3.4289 | 0.5432 | 0.5428 |
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+ | 1.8345 | 10.87 | 5000 | 3.3890 | 0.5502 | 0.5464 |
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+ | 1.711 | 11.96 | 5500 | 3.3365 | 0.5548 | 0.5553 |
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+ | 1.6043 | 13.04 | 6000 | 3.4657 | 0.5529 | 0.5527 |
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+ | 1.4994 | 14.13 | 6500 | 3.3948 | 0.5494 | 0.5500 |
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+ | 1.404 | 15.22 | 7000 | 3.5906 | 0.5529 | 0.5533 |
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+ | 1.3423 | 16.3 | 7500 | 3.5538 | 0.5575 | 0.5555 |
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+ | 1.2991 | 17.39 | 8000 | 3.5762 | 0.5532 | 0.5539 |
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+ | 1.217 | 18.48 | 8500 | 3.6649 | 0.5517 | 0.5518 |
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+ | 1.1763 | 19.57 | 9000 | 3.5238 | 0.5513 | 0.5503 |
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+ | 1.1249 | 20.65 | 9500 | 3.5218 | 0.5436 | 0.5453 |
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+ | 1.0774 | 21.74 | 10000 | 3.7103 | 0.5617 | 0.5622 |
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+ | 1.0558 | 22.83 | 10500 | 3.6698 | 0.5567 | 0.5558 |
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+ | 1.0036 | 23.91 | 11000 | 3.4754 | 0.5648 | 0.5645 |
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+ | 0.9734 | 25.0 | 11500 | 3.5782 | 0.5490 | 0.5483 |
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+ | 0.9614 | 26.09 | 12000 | 3.4920 | 0.5586 | 0.5600 |
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+ | 0.9221 | 27.17 | 12500 | 3.5416 | 0.5440 | 0.5436 |
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+ | 0.905 | 28.26 | 13000 | 3.5065 | 0.5640 | 0.5635 |
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+ | 0.8845 | 29.35 | 13500 | 3.6653 | 0.5463 | 0.5464 |
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+ | 0.8614 | 30.43 | 14000 | 3.5104 | 0.5583 | 0.5571 |
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+ | 0.8414 | 31.52 | 14500 | 3.6002 | 0.5548 | 0.5554 |
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+ | 0.8328 | 32.61 | 15000 | 3.5431 | 0.5544 | 0.5527 |
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+ | 0.8134 | 33.7 | 15500 | 3.5080 | 0.5590 | 0.5585 |
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+ | 0.7973 | 34.78 | 16000 | 3.4150 | 0.5583 | 0.5578 |
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+ | 0.7887 | 35.87 | 16500 | 3.6270 | 0.5486 | 0.5502 |
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+ | 0.7778 | 36.96 | 17000 | 3.6464 | 0.5494 | 0.5491 |
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+ | 0.7662 | 38.04 | 17500 | 3.5100 | 0.5633 | 0.5627 |
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+ | 0.7553 | 39.13 | 18000 | 3.5580 | 0.5532 | 0.5537 |
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+ | 0.7426 | 40.22 | 18500 | 3.4555 | 0.5594 | 0.5583 |
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+ | 0.7494 | 41.3 | 19000 | 3.5871 | 0.5590 | 0.5554 |
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+ | 0.7252 | 42.39 | 19500 | 3.4094 | 0.5590 | 0.5595 |
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+ | 0.7293 | 43.48 | 20000 | 3.4817 | 0.5656 | 0.5661 |
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+ | 0.7103 | 44.57 | 20500 | 3.4964 | 0.5594 | 0.5596 |
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+ | 0.718 | 45.65 | 21000 | 3.4770 | 0.5598 | 0.5593 |
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+ | 0.7147 | 46.74 | 21500 | 3.4938 | 0.5613 | 0.5616 |
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+ | 0.7014 | 47.83 | 22000 | 3.4664 | 0.5571 | 0.5567 |
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+ | 0.6991 | 48.91 | 22500 | 3.4357 | 0.5606 | 0.5606 |
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+ | 0.6944 | 50.0 | 23000 | 3.5245 | 0.5517 | 0.5524 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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+ "XLMRobertaForSequenceClassificationKD"
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+ ],
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+ "id2label": {
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+ "model_type": "xlm-roberta",
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+ "num_attention_heads": 12,
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+ "pad_token_id": 1,
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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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+ "use_cache": true,
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+ "vocab_size": 250002
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+ }
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