GLoVe-LSTM

Model Name:*

Description with Markdown (optional):

# Summary

This model uses GloVe embeddings and is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% on the test set.

[Explore live Sentiment Analysis demo at AllenNLP](https://demo.allennlp.org/sentiment-analysis/glove-sentiment-analysis).

## How do I load this model?

```python
from allennlp_models.pretrained import load_predictor
predictor = load_predictor("glove-sst")
```

### Getting predictions

```python
sentence = "This film doesn't care about cleverness, wit or any other kind of intelligent humor."
preds = predictor.predict(sentence)
print(f"p(positive)={preds['probs'][0]:.2%}")
# prints: p(positive)=15.60%
```

You can also get predictions using allennlp command line interface:

```shell
echo '{"sentence": "This film doesn'\''t care about cleverness, wit or any other kind of intelligent humor."}' | \
    allennlp predict https://storage.googleapis.com/allennlp-public-models/basic_stanford_sentiment_treebank-2020.06.09.tar.gz -
```

## How do I evaluate this model?
To evaluate the model on Stanford Sentiment Treebank run:

```shell
allennlp evaluate https://storage.googleapis.com/allennlp-public-models/basic_stanford_sentiment_treebank-2020.06.09.tar.gz \
    https://allennlp.s3.amazonaws.com/datasets/sst/test.txt
```

## How do I train this model?

To train this model you can use `allennlp` CLI tool and the configuration file [basic_stanford_sentiment_treebank.jsonnet](https://raw.githubusercontent.com/allenai/allennlp-models/v2.1.0/training_config/classification/basic_stanford_sentiment_treebank.jsonnet):

```shell
allennlp train basic_stanford_sentiment_treebank.jsonnet -s output_dir
```

See the [AllenNLP Training and prediction](https://guide.allennlp.org/training-and-prediction#2) guide for more details.

Paper:*

Code URL (optional):

LR	0.001
Epochs	5
Batch Size	32

allenai / allennlp

Summary

How do I load this model?

Getting predictions

How do I evaluate this model?

How do I train this model?

Results

Sentiment Analysis on SST-2 Binary classification

Sentiment Analysis

Training Techniques	Adam
Architecture	LSTM, Linear Layer
LR	0.001
Epochs	5
Batch Size	32
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