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README.md

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----
-license: other
-license_name: gnu-general-public-license
-license_link: LICENSE
----
+# Subject Classifier built on Distilbert
+
+## Table of Contents
+- [Model Details](#model-details)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Environmental Impact](#environmental-impact)
+
+## Model Details
+
+**Model Description:**  This is the [uncased DistilBERT model](https://huggingface.co/distilbert-base-uncased) fine-tuned on a custom dataset that is built on the [IITJEE NEET AIIMS Students Questions Data](https://www.kaggle.com/datasets/mrutyunjaybiswal/iitjee-neet-aims-students-questions-data?resource=download) for the subject classification task. 
+- **Developed by:** The [Typeform](https://www.typeform.com/) team.
+- **Model Type:** Text Classification
+- **Language(s):** English
+- **License:** GNU GENERAL PUBLIC LICENSE
+- **Parent Model:** See the [distilbert base uncased model](https://huggingface.co/distilbert-base-uncased) for more information about the Distilled-BERT base model.
+
+
+## Uses
+This model can be used for text classification tasks.
+
+
+## Risks, Limitations and Biases
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+
+
+## Training
+
+Training is done on a [NVIDIA RTX 3070](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/) [AMD Ryzen 7 5800](https://www.amd.com/en/products/cpu/amd-ryzen-7-5800) with the following hyperparameters:
+
+```
+$ training.ipynb \
+    --model_name_or_path distilbert-base-uncased \
+    --do_train \
+    --do_eval \
+    --max_seq_length 512 \
+    --per_device_train_batch_size 4 \
+    --learning_rate 1e-05 \
+    --num_train_epochs 5 \
+```
+
+## Evaluation
+
+
+#### Evaluation Results
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+Epochs: 5 | Train Loss:  0.001                 | Train Accuracy:  0.989                 | Val Loss:  0.006                 | Val Accuracy:  0.950
+CPU times: user 18h 19min 13s, sys: 1min 34s, total: 18h 20min 47s
+Wall time: 18h 20min 7s
+- **Epoch = ** 5.0
+- **Evaluation Accuracy =**  0.950
+- **Evaluation Loss =** 0.006
+- **Training Accuracy =**  0.989
+- **Training Loss =** 0.001
+
+#### Testing Results
+
+|                 | precision | recall | f1-score | support |
+|-----------------|-----------|--------|----------|---------|
+| biology         | 0.98      | 0.99   | 0.99     | 15988   |
+| chemistry       | 1.00      | 0.99   | 0.99     | 20678   |
+| computer        | 1.00      | 0.99   | 0.99     | 8754    |
+| maths           | 1.00      | 1.00   | 1.00     | 26661   |
+| physics         | 0.99      | 0.98   | 0.99     | 10306   |
+| social sciences | 0.99      | 1.00   | 0.99     | 25695   |
+|                 |           |        |          |         |
+| accuracy        | 0.99      | 108082 |          |         |
+| macro avg       | 0.99      | 0.99   | 0.99     | 108082  |
+| weighted avg    | 0.99      | 0.99   | 0.99     | 108082  |
+
+
+## Environmental Impact
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).
+
+
+**Hardware Type:** 1 NVIDIA RTX 3070 
+
+**Hours used:**  18h 19min 13s
+
+**Carbon Emitted:** (Power consumption x Time x Carbon produced based on location of power grid): Unknown
+
+

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