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euk_roberta_large_essentiality_Network

Text Classification
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roberta
Generated from Trainer
text-embeddings-inference
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sms112/euk_roberta_large_essentiality_Network

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  2. model.safetensors +1 -1
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: roberta-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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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: euk_roberta_large_essentiality_Network
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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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+ # euk_roberta_large_essentiality_Network
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+
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+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4307
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+ - Accuracy: 0.8210
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+ - Precision: 0.7886
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+ - Recall: 0.8771
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+ - F1: 0.8305
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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: 1e-05
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+ - train_batch_size: 60
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+ - eval_batch_size: 60
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 240
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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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+ - num_epochs: 15
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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 | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | No log | 1.0 | 47 | 0.5793 | 0.7023 | 0.7021 | 0.7031 | 0.7026 |
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+ | No log | 2.0 | 94 | 0.4761 | 0.7812 | 0.7861 | 0.7727 | 0.7794 |
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+ | No log | 3.0 | 141 | 0.4792 | 0.7769 | 0.7506 | 0.8295 | 0.7881 |
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+ | No log | 4.0 | 188 | 0.4617 | 0.7822 | 0.7641 | 0.8168 | 0.7896 |
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+ | No log | 5.0 | 235 | 0.4748 | 0.7769 | 0.7393 | 0.8558 | 0.7933 |
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+ | No log | 6.0 | 282 | 0.4401 | 0.7961 | 0.7773 | 0.8303 | 0.8029 |
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+ | No log | 7.0 | 329 | 0.4273 | 0.7968 | 0.7828 | 0.8217 | 0.8018 |
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+ | No log | 8.0 | 376 | 0.4282 | 0.8099 | 0.7825 | 0.8587 | 0.8188 |
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+ | No log | 9.0 | 423 | 0.4242 | 0.8099 | 0.8 | 0.8267 | 0.8131 |
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+ | No log | 10.0 | 470 | 0.4248 | 0.8089 | 0.7908 | 0.8402 | 0.8147 |
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+ | 1.8645 | 11.0 | 517 | 0.4183 | 0.8139 | 0.8095 | 0.8210 | 0.8152 |
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+ | 1.8645 | 12.0 | 564 | 0.4206 | 0.8195 | 0.7988 | 0.8544 | 0.8257 |
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+ | 1.8645 | 13.0 | 611 | 0.4225 | 0.8178 | 0.7985 | 0.8501 | 0.8235 |
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+ | 1.8645 | 14.0 | 658 | 0.4307 | 0.8210 | 0.7886 | 0.8771 | 0.8305 |
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+ | 1.8645 | 15.0 | 705 | 0.4259 | 0.8163 | 0.8016 | 0.8409 | 0.8208 |
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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 2.9.0+cu128
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.2
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