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--- |
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language: |
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- en |
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license: mit |
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base_model: prajjwal1/bert-tiny |
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tags: |
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- pytorch |
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- movie-review-sentiment |
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- BertForSequenceClassification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- matthews_correlation |
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model-index: |
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- name: tiny-imdb |
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results: |
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- task: |
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type: text-classification |
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metrics: |
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- type: accuracy |
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value: 0.8944 |
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name: accuracy |
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- type: accuracy |
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value: 0.7888 |
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name: matthews_correlation |
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datasets: |
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- imdb |
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library_name: transformers |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-tiny-imdb |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2775 |
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- Accuracy: 0.8944 |
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- Matthews Correlation: 0.7888 |
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## Model description |
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This is the smallest version of BERT model suggested by Google in this [GitHub Repo](https://github.com/google-research/bert), this model contains 2 transformer layers and an a hidden layer output length of 128, ie __(L=2, H=128)__. There are a total 4.39 million paramteres in the model. |
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## Intended uses & limitations |
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This model should be used for text classification tasks specifically on movie reviews or other such text data. Also you can use this model for other downstream tasks like: |
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- Sentiment Analysis |
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- Named Entity Recognition or Token Classification |
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This model should not be used for any tasks other than the above mentioned or any language other than English. |
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### How to use the Model |
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__Pytorch Model__ |
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```python |
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from transformers import pipeline |
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# load pipeline |
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tiny_bert = pipeline("text-classification", "arnabdhar/tinybert-imdb") |
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# perform inference |
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results = pipeline(input_text, truncation=True, max_length=128) |
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``` |
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__ONNX Model__ |
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```python |
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from transformers import AutoTokenizer, pipeline |
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from optimum.onnxruntime import ORTModelForSequenceClassification |
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# load tokenizer & model |
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model_name = "arnabdhar/tinybert-imdb" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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onnx_model = ORTModelForSequenceClassification.from_pretrained(model_name) |
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# build pipeline |
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tiny_bert_onnx = pipeline( |
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task = "text-classification", |
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tokenizer = tokenizer, |
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model = onnx_model |
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) |
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# perform inference |
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results = tiny_bert_onnx(input_text, truncation=True, max_length=128) |
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``` |
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## Training |
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The model was finetuned on Google Colab using the NVIDIA V100 GPU and was trained for 9 epochs, it took around 12 minutes to finish finetuning. |
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This model has been trained on the [imdb](https://huggingface.co/datasets/imdb) dataset which has 25,000 data text data for each training set and testing set, but I have combined both the partitions and then split the dataset in 80:20 ratio and used it for finetuning. This approach gave me a larger dataset to finetune the model. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 320 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 9 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------------------:| |
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| 0.4927 | 1.0 | 1250 | 0.3557 | 0.8484 | 0.7016 | |
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| 0.298 | 2.0 | 2500 | 0.2874 | 0.8866 | 0.7732 | |
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| 0.2555 | 3.0 | 3750 | 0.2799 | 0.8912 | 0.7828 | |
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| 0.2132 | 4.0 | 5000 | 0.2775 | 0.8944 | 0.7888 | |
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| 0.1779 | 5.0 | 6250 | 0.3065 | 0.891 | 0.7835 | |
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| 0.1508 | 6.0 | 7500 | 0.3331 | 0.889 | 0.7811 | |
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| 0.1304 | 7.0 | 8750 | 0.3451 | 0.8926 | 0.7870 | |
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| 0.119 | 8.0 | 10000 | 0.3670 | 0.8915 | 0.7852 | |
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| 0.1118 | 9.0 | 11250 | 0.3655 | 0.891 | 0.7840 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
