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End of training

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README.md ADDED
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+ ---
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+ base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: DriveFeelings-Roberta-sentiment-analyzer-for-twitter
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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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+ # DriveFeelings-Roberta-sentiment-analyzer-for-twitter
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+
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+ This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3098
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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: 6
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+ - eval_batch_size: 2
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+ - seed: 42
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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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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:-----:|:---------------:|
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+ | 0.5256 | 1.0 | 3350 | 0.4115 |
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+ | 0.3777 | 2.0 | 6700 | 0.3490 |
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+ | 0.4593 | 3.0 | 10050 | 0.2626 |
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+ | 0.073 | 4.0 | 13400 | 0.3266 |
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+ | 0.1437 | 5.0 | 16750 | 0.3098 |
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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 2.0.1+cu117
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+ - Datasets 2.14.6
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+ - Tokenizers 0.13.3
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+ "_name_or_path": "cardiffnlp/twitter-roberta-base-sentiment-latest",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "2": "positive"
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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": 12,
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+ "num_hidden_layers": 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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+ }
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