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--- |
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license: apache-2.0 |
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base_model: google/bigbird-roberta-base |
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tags: |
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- eduscore |
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- data filter |
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inference: false |
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datasets: |
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- HuggingFaceFW/fineweb-edu-llama3-annotations |
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language: |
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- en |
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--- |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/pszemraj/eduscore-regression/runs/04oc07hx) |
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# bigbird-roberta-base: eduscore |
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Similar to the [original](https://hf.co/HuggingFaceFW/fineweb-edu-classifier), this model predicts a score of 0 to 5 on 'educational quality' of some text. This model was fine-tuned @ its max context length of 4096 tokens. |
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## Usage |
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Note this is for CPU, for GPU you will need to make some (small) changes. |
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```py |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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model_name = "pszemraj/bigbird-roberta-base-edu-classifier" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained( |
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model_name, attn_implementation="eager" |
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) |
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text = "This is a test sentence." |
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inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True) |
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outputs = model(**inputs) |
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logits = outputs.logits.squeeze(-1).float().detach().numpy() |
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score = logits.item() |
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result = { |
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"text": text, |
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"score": score, |
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"int_score": int(round(max(0, min(score, 5)))), |
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} |
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print(result) |
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# {'text': 'This is a test sentence.', 'score': 0.20170727372169495, 'int_score': 0} |
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``` |
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## Details |
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This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2176 |
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- Mse: 0.2176 |
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## Intended uses & limitations |
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Refer to the hf classifier's [model card](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier#limitations) for more details |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 90085 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 1.0 |
