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--- |
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license: apache-2.0 |
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base_model: albert-base-v2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- squad |
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model-index: |
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- name: albert-base-v2-finetuned-squad |
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results: |
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- task: |
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name: Question Answering |
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type: question-answering |
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dataset: |
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type: squad_v2 |
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name: The Stanford Question Answering Dataset |
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args: en |
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metrics: |
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- type: eval_exact |
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value: 76.263 |
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- type: eval_f1 |
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value: 84.734 |
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language: |
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- en |
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metrics: |
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- exact_match |
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- f1 |
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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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# albert-base-v2-finetuned-squad |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4539 |
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- Exact Match: 80.60548722800378 |
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- F1 score: 88.76870326468953 |
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## Model description |
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This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- SQuAD2.0. |
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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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Training and evaluation was done on SQuAD2.0. |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.8702 | 1.0 | 5540 | 0.8943 | |
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| 0.6972 | 2.0 | 11080 | 0.9087 | |
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| 0.4998 | 3.0 | 16620 | 0.9890 | |
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| 0.3601 | 4.0 | 22160 | 1.1892 | |
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| 0.235 | 5.0 | 27700 | 1.4539 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 1.12.1 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
