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BERTweet-large with correct labels on full match data

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  1. README.md +66 -0
  2. config.json +45 -0
  3. model.safetensors +3 -0
  4. tokenizer.json +0 -0
  5. tokenizer_config.json +16 -0
  6. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: vinai/bertweet-large
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: BERTweet-large-full-match-data
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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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+ # BERTweet-large-full-match-data
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+
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+ This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3109
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+ - Accuracy: 0.8920
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+ - F1 Macro: 0.8860
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+ - F1 Weighted: 0.8920
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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: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 200
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+ - num_epochs: 2
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+ - mixed_precision_training: Native AMP
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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 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
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+ | 0.3837 | 1.0 | 749 | 0.3534 | 0.8745 | 0.8636 | 0.8751 |
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+ | 0.2162 | 2.0 | 1498 | 0.3109 | 0.8920 | 0.8860 | 0.8920 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 5.0.0
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+ - Pytorch 2.10.0+cu128
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.2
config.json ADDED
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+ {
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+ "add_cross_attention": false,
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+ "architectures": [
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+ "RobertaForSequenceClassification"
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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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+ "dtype": "float32",
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+ "eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "Opinion",
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+ "1": "Argumentative",
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+ "2": "Expressive",
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+ "3": "Informational",
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+ "4": "Neutral"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "is_decoder": false,
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+ "label2id": {
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+ "Argumentative": 1,
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+ "Expressive": 2,
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+ "Informational": 3,
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+ "Neutral": 4,
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+ "Opinion": 0
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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": "roberta",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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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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+ "tie_word_embeddings": true,
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+ "transformers_version": "5.0.0",
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+ "type_vocab_size": 1,
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+ "use_cache": false,
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+ "vocab_size": 50265
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+ }
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+ "tokenizer_class": "RobertaTokenizer",
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+ "trim_offsets": true,
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+ "unk_token": "<unk>"
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+ }
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