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README.md

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+---
+language: 
+- en
+thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
+tags:
+- question-answering
+license: apache-2.0
+datasets:
+- squad
+metrics:
+- squad
+---
+
+# DistilBERT with a second step of distillation
+
+## Model description
+
+This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
+
+In this version, the following pre-trained models were used:
+
+* Student: `distilbert-base-uncased`
+* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
+
+## Training data
+
+This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
+
+```python
+from datasets import load_dataset
+squad = load_dataset('squad')
+```
+
+## Training procedure
+
+## Eval results
+
+|                  | Exact Match | F1   |
+|------------------|-------------|------|
+| DistilBERT paper | 79.1        | 86.9 |
+| Ours             | 78.4        | 86.5 |
+
+The scores were calculated using the `squad` metric from `datasets`.
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{sanh2020distilbert,
+      title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, 
+      author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
+      year={2020},
+      eprint={1910.01108},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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training_args.bin

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