
# 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
{"language": ["en"], "datasets": ["imdb"], "metrics": ["accuracy"]}
transformers pytorch jax safetensors bert text-classification en dataset:imdb autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #en #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us 


# 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



#### 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

Fine tuned on 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
# 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 #### 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

Fine tuned on 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
TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #en #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us 
 # 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 #### 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

Fine tuned on 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
passage: TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #en #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us 
# 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#### 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

Fine tuned on 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