File size: 1,721 Bytes
af21b1e c611a53 af21b1e c611a53 af21b1e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
This model provides a MobileBERT [(Sun et al., 2020)](https://arxiv.org/abs/2004.02984) fine-tuned on the SST data with three sentiments (0 -- negative, 1 -- neutral, and 2 -- positive).
## Example Usage
Below, we provide illustrations on how to use this model to make sentiment predictions.
```python
import torch
from transformers import AutoTokenizer, AutoConfig, MobileBertForSequenceClassification
# load model
model_name = r'cambridgeltl/sst_mobilebert-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
model = MobileBertForSequenceClassification.from_pretrained(model_name, config=config)
model.eval()
'''
labels:
0 -- negative
1 -- neutral
2 -- positive
'''
# prepare exemplar sentences
batch_sentences = [
"in his first stab at the form , jacquot takes a slightly anarchic approach that works only sporadically .",
"a valueless kiddie paean to pro basketball underwritten by the nba .",
"a very well-made , funny and entertaining picture .",
]
# prepare input
inputs = tokenizer(batch_sentences, max_length=256, truncation=True, padding=True, return_tensors='pt')
input_ids, attention_mask = inputs.input_ids, inputs.attention_mask
# make predictions
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
predictions = torch.argmax(outputs.logits, dim = -1)
print (predictions)
# tensor([1, 0, 2])
```
## Citation:
If you find this model useful, please kindly cite our model as
```bibtex
@misc{susstmobilebert,
author = {Su, Yixuan},
title = {A MobileBERT Fine-tuned on SST},
howpublished = {\url{https://huggingface.co/cambridgeltl/sst_mobilebert-uncased}},
year = 2022
}
``` |