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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: distilbert-base-gest-pred-seqeval-partialmatch
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results: []
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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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# distilbert-base-gest-pred-seqeval-partialmatch
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7454
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- Precision: 0.8160
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- Recall: 0.7114
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- F1: 0.7404
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- Accuracy: 0.8264
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 16
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- eval_batch_size: 16
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 1.5808 | 1.0 | 147 | 1.0620 | 0.3787 | 0.3887 | 0.3730 | 0.7308 |
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| 0.9003 | 2.0 | 294 | 0.8608 | 0.6557 | 0.5776 | 0.5752 | 0.7802 |
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| 0.6645 | 3.0 | 441 | 0.7206 | 0.6279 | 0.5758 | 0.5768 | 0.7945 |
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| 0.5084 | 4.0 | 588 | 0.6758 | 0.7613 | 0.6761 | 0.7005 | 0.8114 |
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| 0.3961 | 5.0 | 735 | 0.6692 | 0.8146 | 0.7148 | 0.7431 | 0.8270 |
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| 0.2992 | 6.0 | 882 | 0.7142 | 0.8092 | 0.6857 | 0.7128 | 0.8192 |
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| 0.248 | 7.0 | 1029 | 0.6920 | 0.8167 | 0.7075 | 0.7395 | 0.8218 |
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| 0.1984 | 8.0 | 1176 | 0.7146 | 0.8149 | 0.7297 | 0.7522 | 0.8205 |
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| 0.1639 | 9.0 | 1323 | 0.7357 | 0.8175 | 0.7079 | 0.7401 | 0.8270 |
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| 0.1499 | 10.0 | 1470 | 0.7454 | 0.8160 | 0.7114 | 0.7404 | 0.8264 |
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### Framework versions
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- Transformers 4.27.3
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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