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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: distilbert-base-uncased-xsum-factuality |
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results: [] |
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datasets: |
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- xsum_factuality |
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language: |
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- en |
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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-uncased-xsum-factuality |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [XSum-Factuality](https://huggingface.co/datasets/xsum_factuality) dataset. |
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You can view more implementation details as part of this [GitHub](https://github.com/ernlavr/llamarizer) repository. It achieves the following results on the evaluation set: |
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- Loss: 0.6850 |
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- Accuracy: 0.6332 |
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- F1: 0.6212 |
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- Precision: 0.6526 |
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- Recall: 0.6332 |
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# Weights and Biases Documentation |
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View the full run on [Weights & Biases](https://wandb.ai/ernlavr/adv_nlp2023/runs/fqluc2vb) |
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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: 1e-06 |
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- train_batch_size: 2 |
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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_ratio: 0.1 |
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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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| 0.6904 | 6.93 | 1040 | 0.6850 | 0.6332 | 0.6212 | 0.6526 | 0.6332 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
