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doc-topic-model_eval-00_train-04

Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
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avsolatorio/doc-topic-model_eval-00_train-04

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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: microsoft/deberta-v3-small
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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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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: doc-topic-model_eval-00_train-04
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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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+ # doc-topic-model_eval-00_train-04
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0374
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+ - Accuracy: 0.9874
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+ - F1: 0.6313
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+ - Precision: 0.6913
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+ - Recall: 0.5809
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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: 4
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+ - eval_batch_size: 256
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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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+ - num_epochs: 100
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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 | Precision | Recall |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.0934 | 0.4929 | 1000 | 0.0901 | 0.9815 | 0.0 | 0.0 | 0.0 |
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+ | 0.0778 | 0.9857 | 2000 | 0.0700 | 0.9815 | 0.0 | 0.0 | 0.0 |
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+ | 0.0618 | 1.4786 | 3000 | 0.0565 | 0.9829 | 0.1733 | 0.8388 | 0.0967 |
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+ | 0.0535 | 1.9714 | 4000 | 0.0489 | 0.9844 | 0.3322 | 0.7953 | 0.2100 |
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+ | 0.0473 | 2.4643 | 5000 | 0.0452 | 0.9857 | 0.4684 | 0.7474 | 0.3411 |
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+ | 0.0436 | 2.9571 | 6000 | 0.0421 | 0.9862 | 0.4998 | 0.7577 | 0.3729 |
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+ | 0.0389 | 3.4500 | 7000 | 0.0403 | 0.9866 | 0.5333 | 0.7517 | 0.4132 |
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+ | 0.0376 | 3.9428 | 8000 | 0.0395 | 0.9867 | 0.5632 | 0.7194 | 0.4628 |
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+ | 0.0339 | 4.4357 | 9000 | 0.0386 | 0.9869 | 0.5613 | 0.7431 | 0.4509 |
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+ | 0.0337 | 4.9285 | 10000 | 0.0381 | 0.9873 | 0.5768 | 0.7512 | 0.4681 |
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+ | 0.0295 | 5.4214 | 11000 | 0.0371 | 0.9872 | 0.6005 | 0.7141 | 0.5182 |
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+ | 0.0305 | 5.9142 | 12000 | 0.0377 | 0.9874 | 0.5996 | 0.7307 | 0.5084 |
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+ | 0.0254 | 6.4071 | 13000 | 0.0373 | 0.9875 | 0.6118 | 0.7224 | 0.5306 |
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+ | 0.0273 | 6.9000 | 14000 | 0.0373 | 0.9876 | 0.6137 | 0.7237 | 0.5327 |
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+ | 0.0228 | 7.3928 | 15000 | 0.0371 | 0.9878 | 0.6180 | 0.7331 | 0.5341 |
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+ | 0.0235 | 7.8857 | 16000 | 0.0374 | 0.9874 | 0.6313 | 0.6913 | 0.5809 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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