Spaces:
Sleeping
title: Movie Review V2
emoji: 🚀
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 3.15.0
app_file: app.py
pinned: false
license: apache-2.0
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
This app asks the user to input a movie review as a text string, and predicts whether the sentiment of the review is 'Positive' or 'Negative'.
The prediction is made using a Bidirectional Encoder Representations from Transformers (BERT) model, namely a fine-tuned version of DistilBERT (https://arxiv.org/abs/1910.01108)
We started with the DistilBertForSequenceClassification pre-trained model in the Hugging Face transformers library (https://huggingface.co/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification) and DistilBertTokenizerFast (https://huggingface.co/docs/transformers/v4.25.1/en/model_doc/distilbert#transformers.DistilBertTokenizerFast)
We fine-tuned the model using the IMDb Large Movie Review Dataset (https://ai.stanford.edu/~amaas/data/sentiment/) for 3 epochs using batch sizes of 16 samples and a learning rate of 1e-5. The loss and accuracy progressed as follows:
Epoch 0001 of 0003, batch 0000 of 2188 === Loss: 0.6800
Epoch 0001 of 0003, batch 0250 of 2188 === Loss: 0.2488
Epoch 0001 of 0003, batch 0500 of 2188 === Loss: 0.4501
Epoch 0001 of 0003, batch 0750 of 2188 === Loss: 0.1309
Epoch 0001 of 0003, batch 1000 of 2188 === Loss: 0.4273
Epoch 0001 of 0003, batch 1250 of 2188 === Loss: 0.3193
Epoch 0001 of 0003, batch 1500 of 2188 === Loss: 0.5093
Epoch 0001 of 0003, batch 1750 of 2188 === Loss: 0.4583
Epoch 0001 of 0003, batch 2000 of 2188 === Loss: 0.3154
Training accuracy: 96.62 === Valid accuracy: 92.54
Epoch 0002 of 0003, batch 0000 of 2188 === Loss: 0.1179
Epoch 0002 of 0003, batch 0250 of 2188 === Loss: 0.0136
Epoch 0002 of 0003, batch 0500 of 2188 === Loss: 0.1435
Epoch 0002 of 0003, batch 0750 of 2188 === Loss: 0.0454
Epoch 0002 of 0003, batch 1000 of 2188 === Loss: 0.0768
Epoch 0002 of 0003, batch 1250 of 2188 === Loss: 0.2802
Epoch 0002 of 0003, batch 1500 of 2188 === Loss: 0.0200
Epoch 0002 of 0003, batch 1750 of 2188 === Loss: 0.1257
Epoch 0002 of 0003, batch 2000 of 2188 === Loss: 0.1308
Training accuracy: 98.76 === Valid accuracy: 92.46
Epoch 0003 of 0003, batch 0000 of 2188 === Loss: 0.0074
Epoch 0003 of 0003, batch 0250 of 2188 === Loss: 0.0039
Epoch 0003 of 0003, batch 0500 of 2188 === Loss: 0.0611
Epoch 0003 of 0003, batch 0750 of 2188 === Loss: 0.0306
Epoch 0003 of 0003, batch 1000 of 2188 === Loss: 0.1513
Epoch 0003 of 0003, batch 1250 of 2188 === Loss: 0.0014
Epoch 0003 of 0003, batch 1500 of 2188 === Loss: 0.0020
Epoch 0003 of 0003, batch 1750 of 2188 === Loss: 0.1905
Epoch 0003 of 0003, batch 2000 of 2188 === Loss: 0.1545
Training accuracy: 99.43 === Valid accuracy: 92.38