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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: distilbert-base-multilingual-cased |
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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: multilabel_lora_distilbert_runews_classifier_tuned |
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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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# multilabel_lora_distilbert_runews_classifier_tuned |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0019 |
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- Accuracy: 0.8276 |
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- F1: 0.8284 |
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- Precision: 0.8317 |
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- Recall: 0.8276 |
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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: 0.0009143508688456378 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 91 | 0.5987 | 0.7634 | 0.7621 | 0.7648 | 0.7634 | |
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| No log | 2.0 | 182 | 0.3768 | 0.8693 | 0.8698 | 0.8767 | 0.8693 | |
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| No log | 3.0 | 273 | 0.2620 | 0.9065 | 0.9063 | 0.9093 | 0.9065 | |
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| No log | 4.0 | 364 | 0.2427 | 0.9202 | 0.9203 | 0.9220 | 0.9202 | |
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| No log | 5.0 | 455 | 0.2244 | 0.9367 | 0.9369 | 0.9387 | 0.9367 | |
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| 0.3641 | 6.0 | 546 | 0.2385 | 0.9491 | 0.9491 | 0.9495 | 0.9491 | |
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| 0.3641 | 7.0 | 637 | 0.2560 | 0.9464 | 0.9464 | 0.9465 | 0.9464 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
