| thon | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact") | |
| model = AutoModelForSequenceClassification.from_pretrained("microsoft/tapex-large-finetuned-tabfact") | |
| prepare table + sentence | |
| data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]} | |
| table = pd.DataFrame.from_dict(data) | |
| sentence = "George Clooney has 30 movies" | |
| encoding = tokenizer(table, sentence, return_tensors="pt") | |
| forward pass | |
| outputs = model(**encoding) | |
| print prediction | |
| predicted_class_idx = outputs.logits[0].argmax(dim=0).item() | |
| print(model.config.id2label[predicted_class_idx]) | |
| Refused | |
| TAPEX architecture is the same as BART, except for tokenization. |