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--- |
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license: apache-2.0 |
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language: en |
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library: transformers |
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other: distilbert |
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datasets: |
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- Short Question Answer Assessment Dataset |
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--- |
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# DistilBERT base uncased model for Short Question Answer Assessment |
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## Model description |
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DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a |
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self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, |
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with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic |
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process to generate inputs and labels from those texts using the BERT base model. |
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This is a classification model that solves Short Question Answer Assessment task, finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-uncased) on |
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[Question Answer Assessment dataset](#) |
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## Intended uses & limitations |
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This can only be used for the kind of questions and answers provided by that are similar to the ones in the dataset of [Banjade et al.](https://aclanthology.org/W16-0520.pdf). |
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### How to use |
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You can use this model directly with a : |
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```python |
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>>> from transformers import pipeline |
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>>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment", return_all_scores=True) |
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>>> context = "To rescue a child who has fallen down a well, rescue workers fasten him to a rope, the other end of which is then reeled in by a machine. The rope pulls the child straight upward at steady speed." |
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>>> question = "How does the amount of tension in the rope compare to the downward force of gravity acting on the child?" |
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>>> ref_answer = "Since the child is being raised straight upward at a constant speed, the net force on the child is zero and all the forces balance. That means that the tension in the rope balances the downward force of gravity." |
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>>> student_answer = "The tension force is higher than the force of gravity." |
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>>> |
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>>> body = " [SEP] ".join([context, question, ref_answer, student_answer]) |
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>>> raw_results = classifier([body]) |
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>>> raw_results |
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[[{'label': 'LABEL_0', 'score': 0.0004029414849355817}, |
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{'label': 'LABEL_1', 'score': 0.0005476847873069346}, |
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{'label': 'LABEL_2', 'score': 0.998059093952179}, |
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{'label': 'LABEL_3', 'score': 0.0009902542224153876}]] |
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>>> _LABELS_ID2NAME = {0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"} |
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>>> results = [] |
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>>> for result in raw_results: |
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for score in result: |
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results.append([ |
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{_LABELS_ID2NAME[int(score["label"][-1:])]: "%.2f" % score["score"]} |
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]) |
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>>> results |
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[[{'correct': '0.00'}], |
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[{'correct_but_incomplete': '0.00'}], |
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[{'contradictory': '1.00'}], |
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[{'incorrect': '0.00'}]] |
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``` |
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### Limitations and bias |
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Even if the training data used for this model could be characterized as fairly neutral, this model can have biased |
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predictions. It also inherits some of |
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[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). |
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This bias will also affect all fine-tuned versions of this model. |
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Also one of the limiations of this model is the length, longer sequences would lead to wrong predictions, due to the pre-processing phase (after concatentating the input sequences, the important student answer might be pruned!) |
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## Pre-training data |
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DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset |
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consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) |
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(excluding lists, tables and headers). |
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## Fine-tuning data |
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The annotated dataset consists of 900 students’ short constructed answers and their correctness in the given context. Four qualitative levels of correctness are defined, correct, correct-but-incomplete, contradictory and Incorrect. |
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## Training procedure |
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### Preprocessing |
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In the preprocessing phase, the following parts are concatenated: _question context_, _question_, _reference_answer_, and _student_answer_ using the separator `[SEP]`. |
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This makes the full text as: |
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``` |
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[CLS] Context Sentence [SEP] Question Sentence [SEP] Reference Answer Sentence [SEP] Student Answer Sentence [CLS] |
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``` |
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The data are splitted according to the following ratio: |
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- Training set 80%. |
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- Test set 20%. |
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Lables are mapped as: `{0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"}` |
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### Fine-tuning |
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The model was finetuned on GeForce GTX 960M for 20 minuts. The parameters are: |
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| Parameter | Value | |
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|:-------------------:|:-----:| |
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| Learning rate | 5e-5 | |
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| Weight decay | 0.01 | |
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| Training batch size | 8 | |
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| Epochs | 4 | |
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Here is the scores during the training: |
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| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:----------:|:-------------:|:-----------------:|:----------:|:---------:|:----------:|:--------:| |
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| 1 | No log | 0.665765 | 0.755330 | 0.743574 | 0.781210 | 0.755330 | |
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| 2 | 0.932100 | 0.362124 | 0.890355 | 0.889875 | 0.891407 | 0.890355 | |
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| 3 | 0.364900 | 0.226225 | 0.942132 | 0.941802 | 0.942458 | 0.942132 | |
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| 3 | 0.176900 | 0.193660 | 0.954315 | 0.954175 | 0.954985 | 0.954315 | |
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## Evaluation results |
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When fine-tuned on downstream task of Question Answer Assessment, 4 class classification, this model achieved the following results: |
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(scores are rounded to 2 floating points) |
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| | precision | recall | f1-score | support | |
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|:------------------------:|:----------:|:-------:|:--------:|:-------:| |
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| _correct_ | 0.938 | 0.989 | 0.963 | 366 | |
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| _correct_but_incomplete_ | 0.975 | 0.922 | 0.948 | 257 | |
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| _contradictory_ | 0.946 | 0.938 | 0.942 | 113 | |
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| _incorrect_ | 0.963 | 0.944 | 0.953 | 249 | |
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| accuracy | - | - | 0.954 | 985 | |
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| macro avg | 0.956 | 0.948 | 0.952 | 985 | |
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| weighted avg | 0.955 | 0.954 | 0.954 | 985 | |
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Confision matrix: |
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| Actual \ Predicted | _correct_ | _correct_but_incomplete_ | _contradictory_ | _incorrect_ | |
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|:------------------------:|:---------:|:------------------------:|:---------------:|:-----------:| |
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| _correct_ | 362 | 4 | 0 | 0 | |
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| _correct_but_incomplete_ | 13 | 237 | 0 | 7 | |
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| _contradictory_ | 4 | 1 | 106 | 2 | |
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| _incorrect_ | 7 | 1 | 6 | 235 | |
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The AUC score is: 'micro'= **0.9695** and 'macro': **0.9659** |
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