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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- boolq |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-large_boolq |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: boolq |
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type: boolq |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8834862385321101 |
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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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# deberta-v3-large_boolq |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the boolq dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4601 |
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- Accuracy: 0.8835 |
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## Model description |
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More information needed |
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## Example |
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``` |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("nfliu/deberta-v3-large_boolq") |
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tokenizer = AutoTokenizer.from_pretrained("nfliu/deberta-v3-large_boolq") |
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# Each example is a (question, context) pair. |
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examples = [ |
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("Lake Tahoe is in California", "Lake Tahoe is a popular tourist spot in California."), |
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("Water is wet", "Contrary to popular belief, water is not wet.") |
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] |
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encoded_input = tokenizer(examples, padding=True, truncation=True, return_tensors="pt") |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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probabilities = torch.softmax(model_output.logits, dim=-1).cpu().tolist() |
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probability_no = [round(prob[0], 2) for prob in probabilities] |
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probability_yes = [round(prob[1], 2) for prob in probabilities] |
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for example, p_no, p_yes in zip(examples, probability_no, probability_yes): |
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print(f"Question: {example[0]}") |
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print(f"Context: {example[1]}") |
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print(f"p(No | question, context): {p_no}") |
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print(f"p(Yes | question, context): {p_yes}") |
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print() |
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``` |
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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-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.85 | 250 | 0.5306 | 0.8823 | |
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| 0.1151 | 1.69 | 500 | 0.4601 | 0.8835 | |
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| 0.1151 | 2.54 | 750 | 0.5897 | 0.8792 | |
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| 0.0656 | 3.39 | 1000 | 0.6477 | 0.8804 | |
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| 0.0656 | 4.24 | 1250 | 0.6847 | 0.8838 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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