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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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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/k6z0kenz) |
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# distilbert-base-uncased: edu classifier |
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> [!IMPORTANT] |
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> This is a (rare) encoder that supports flash attention 2! |
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> Use `attn_implementation="flash_attention_2"` when loading w/ [FA2 installed](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features) |
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> for faster inference. |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 0.2324 |
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- Mse: 0.2324 |
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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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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("pszemraj/mpnet-base-edu-classifier") |
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model = AutoModelForSequenceClassification.from_pretrained("pszemraj/mpnet-base-edu-classifier") |
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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': 0.3350256383419037, 'int_score': 0} |
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``` |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 90085 |
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- gradient_accumulation_steps: 8 |
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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 |
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