Update README.md

README.md

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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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+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'.
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+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)
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+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)
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+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:
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+Epoch 0001 of 0003, batch 0000 of 2188 === Loss: 0.6800
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+Epoch 0001 of 0003, batch 0250 of 2188 === Loss: 0.2488
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+Epoch 0001 of 0003, batch 0500 of 2188 === Loss: 0.4501
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+Epoch 0001 of 0003, batch 0750 of 2188 === Loss: 0.1309
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+Epoch 0001 of 0003, batch 1000 of 2188 === Loss: 0.4273
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+Epoch 0001 of 0003, batch 1250 of 2188 === Loss: 0.3193
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+Epoch 0001 of 0003, batch 1500 of 2188 === Loss: 0.5093
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+Epoch 0001 of 0003, batch 1750 of 2188 === Loss: 0.4583
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+Epoch 0001 of 0003, batch 2000 of 2188 === Loss: 0.3154
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+Training accuracy: 96.62 === Valid accuracy: 92.54
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+Epoch 0002 of 0003, batch 0000 of 2188 === Loss: 0.1179
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+Epoch 0002 of 0003, batch 0250 of 2188 === Loss: 0.0136
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+Epoch 0002 of 0003, batch 0500 of 2188 === Loss: 0.1435
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+Epoch 0002 of 0003, batch 0750 of 2188 === Loss: 0.0454
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+Epoch 0002 of 0003, batch 1000 of 2188 === Loss: 0.0768
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+Epoch 0002 of 0003, batch 1250 of 2188 === Loss: 0.2802
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+Epoch 0002 of 0003, batch 1500 of 2188 === Loss: 0.0200
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+Epoch 0002 of 0003, batch 1750 of 2188 === Loss: 0.1257
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+Epoch 0002 of 0003, batch 2000 of 2188 === Loss: 0.1308
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+Training accuracy: 98.76 === Valid accuracy: 92.46
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+Epoch 0003 of 0003, batch 0000 of 2188 === Loss: 0.0074
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+Epoch 0003 of 0003, batch 0250 of 2188 === Loss: 0.0039
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+Epoch 0003 of 0003, batch 0500 of 2188 === Loss: 0.0611
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+Epoch 0003 of 0003, batch 0750 of 2188 === Loss: 0.0306
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+Epoch 0003 of 0003, batch 1000 of 2188 === Loss: 0.1513
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+Epoch 0003 of 0003, batch 1250 of 2188 === Loss: 0.0014
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+Epoch 0003 of 0003, batch 1500 of 2188 === Loss: 0.0020
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+Epoch 0003 of 0003, batch 1750 of 2188 === Loss: 0.1905
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+Epoch 0003 of 0003, batch 2000 of 2188 === Loss: 0.1545
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+Training accuracy: 99.43 === Valid accuracy: 92.38
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