| 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 | |
| } | |
| ``` |