model card

README.md

@@ -0,0 +1,63 @@
+---
+language: 
+- en
+datasets:
+- imdb
+metrics:
+- accuracy
+---
+
+# bert-imdb-1hidden
+
+## Model description
+
+A `bert-base-uncased` model was restricted to 1 hidden layer and
+fine-tuned for sequence classification on the 
+imdb dataset loaded using the `datasets` library.
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+pretrained = "lannelin/bert-imdb-1hidden"
+
+tokenizer = AutoTokenizer.from_pretrained(pretrained)
+
+model = AutoModelForSequenceClassification.from_pretrained(pretrained)
+
+LABELS = ["negative", "positive"]
+
+def get_sentiment(text: str):
+    inputs = tokenizer.encode_plus(text, return_tensors='pt')
+
+    output = model(**inputs)[0].squeeze()
+
+    return LABELS[(output.argmax())]
+
+print(get_sentiment("What a terrible film!"))
+```
+
+#### Limitations and bias
+
+No special consideration given to limitations and bias. 
+
+Any bias held by the imdb dataset may be reflected in the model's output.
+
+## Training data
+
+Initialised with [bert-base-uncased](https://huggingface.co/bert-base-uncased)
+
+Fine tuned on [imdb](https://huggingface.co/datasets/imdb)
+
+
+## Training procedure
+
+ The model was fine-tuned for 1 epoch with a batch size of 64, 
+ a learning rate of 5e-5, and a maximum sequence length of 512.
+
+## Eval results
+
+Accuracy on imdb test set: 0.87132