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

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PyTorch
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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  1. README.md +125 -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: xlm-roberta-base
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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-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: tweet_sentiment_multilingual
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+ type: tweet_sentiment_multilingual
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+ config: all
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+ split: validation
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+ args: all
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5767746913580247
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+ - name: F1
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+ type: f1
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+ value: 0.5751836259585372
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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-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
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+
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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: 4.1676
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+ - Accuracy: 0.5768
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+ - F1: 0.5752
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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: 1123
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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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+ | 1.0487 | 1.09 | 500 | 0.9666 | 0.5305 | 0.5194 |
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+ | 0.9092 | 2.17 | 1000 | 0.9220 | 0.5760 | 0.5733 |
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+ | 0.76 | 3.26 | 1500 | 1.0464 | 0.5791 | 0.5681 |
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+ | 0.6233 | 4.35 | 2000 | 1.1732 | 0.5864 | 0.5809 |
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+ | 0.4852 | 5.43 | 2500 | 1.1695 | 0.5590 | 0.5578 |
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+ | 0.374 | 6.52 | 3000 | 1.3903 | 0.5691 | 0.5669 |
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+ | 0.2851 | 7.61 | 3500 | 1.5832 | 0.5760 | 0.5711 |
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+ | 0.2203 | 8.7 | 4000 | 1.6098 | 0.5737 | 0.5739 |
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+ | 0.1863 | 9.78 | 4500 | 1.9189 | 0.5656 | 0.5566 |
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+ | 0.1437 | 10.87 | 5000 | 2.1445 | 0.5826 | 0.5783 |
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+ | 0.1302 | 11.96 | 5500 | 1.9960 | 0.5791 | 0.5723 |
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+ | 0.1075 | 13.04 | 6000 | 2.5978 | 0.5710 | 0.5663 |
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+ | 0.0957 | 14.13 | 6500 | 2.9129 | 0.5675 | 0.5682 |
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+ | 0.0898 | 15.22 | 7000 | 2.8487 | 0.5799 | 0.5780 |
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+ | 0.082 | 16.3 | 7500 | 2.8461 | 0.5714 | 0.5621 |
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+ | 0.0673 | 17.39 | 8000 | 2.8416 | 0.5849 | 0.5767 |
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+ | 0.0647 | 18.48 | 8500 | 3.1083 | 0.5849 | 0.5810 |
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+ | 0.0597 | 19.57 | 9000 | 2.9063 | 0.5772 | 0.5700 |
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+ | 0.0508 | 20.65 | 9500 | 3.1069 | 0.5706 | 0.5663 |
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+ | 0.0492 | 21.74 | 10000 | 3.1434 | 0.5841 | 0.5853 |
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+ | 0.0485 | 22.83 | 10500 | 2.9341 | 0.5887 | 0.5816 |
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+ | 0.0373 | 23.91 | 11000 | 3.2828 | 0.5810 | 0.5807 |
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+ | 0.0352 | 25.0 | 11500 | 3.1742 | 0.5864 | 0.5802 |
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+ | 0.0326 | 26.09 | 12000 | 3.2767 | 0.5733 | 0.5734 |
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+ | 0.0269 | 27.17 | 12500 | 3.5101 | 0.5826 | 0.5797 |
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+ | 0.0338 | 28.26 | 13000 | 3.2453 | 0.5725 | 0.5693 |
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+ | 0.0289 | 29.35 | 13500 | 3.3957 | 0.5694 | 0.5703 |
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+ | 0.0232 | 30.43 | 14000 | 3.4668 | 0.5710 | 0.5714 |
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+ | 0.0215 | 31.52 | 14500 | 3.5250 | 0.5721 | 0.5660 |
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+ | 0.0197 | 32.61 | 15000 | 3.5990 | 0.5787 | 0.5755 |
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+ | 0.0138 | 33.7 | 15500 | 3.7731 | 0.5745 | 0.5682 |
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+ | 0.0177 | 34.78 | 16000 | 3.6367 | 0.5698 | 0.5671 |
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+ | 0.0145 | 35.87 | 16500 | 3.8987 | 0.5725 | 0.5705 |
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+ | 0.013 | 36.96 | 17000 | 3.8459 | 0.5745 | 0.5737 |
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+ | 0.0133 | 38.04 | 17500 | 3.7106 | 0.5733 | 0.5711 |
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+ | 0.0095 | 39.13 | 18000 | 3.8834 | 0.5683 | 0.5688 |
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+ | 0.0091 | 40.22 | 18500 | 3.9118 | 0.5733 | 0.5731 |
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+ | 0.0107 | 41.3 | 19000 | 3.9038 | 0.5768 | 0.5733 |
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+ | 0.0089 | 42.39 | 19500 | 3.8957 | 0.5826 | 0.5784 |
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+ | 0.0042 | 43.48 | 20000 | 4.1050 | 0.5775 | 0.5761 |
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+ | 0.0067 | 44.57 | 20500 | 4.0982 | 0.5756 | 0.5739 |
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+ | 0.0042 | 45.65 | 21000 | 4.2051 | 0.5737 | 0.5733 |
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+ | 0.0057 | 46.74 | 21500 | 4.1266 | 0.5764 | 0.5764 |
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+ | 0.0056 | 47.83 | 22000 | 4.1318 | 0.5787 | 0.5765 |
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+ | 0.0034 | 48.91 | 22500 | 4.1443 | 0.5791 | 0.5772 |
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+ | 0.003 | 50.0 | 23000 | 4.1676 | 0.5768 | 0.5752 |
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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
config.json ADDED
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+ {
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+ "_name_or_path": "xlm-roberta-base",
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+ "architectures": [
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+ "XLMRobertaForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ },
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "LABEL_2": 2
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "xlm-roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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+ "output_past": true,
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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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+ "transformers_version": "4.33.3",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 250002
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+ }
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