1.18 kB
| This model corresponds to **tapas_masklm_medium_reset** of the [original repository](https://github.com/google-research/tapas). | |
| Here's how you can use it: | |
| ```python | |
| from transformers import TapasTokenizer, TapasForMaskedLM | |
| import pandas as pd | |
| import torch | |
| tokenizer = TapasTokenizer.from_pretrained("google/tapas-medium-masklm") | |
| model = TapasForMaskedLM.from_pretrained("google/tapas-medium-masklm") | |
| data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], | |
| 'Age': ["56", "45", "59"], | |
| 'Number of movies': ["87", "53", "69"] | |
| } | |
| table = pd.DataFrame.from_dict(data) | |
| query = "How many movies has Leonardo [MASK] Caprio played in?" | |
| # prepare inputs | |
| inputs = tokenizer(table=table, queries=query, padding="max_length", return_tensors="pt") | |
| # forward pass | |
| outputs = model(**inputs) | |
| # return top 5 values and predictions | |
| masked_index = torch.nonzero(inputs.input_ids.squeeze() == tokenizer.mask_token_id, as_tuple=False) | |
| logits = outputs.logits[0, masked_index.item(), :] | |
| probs = logits.softmax(dim=0) | |
| values, predictions = probs.topk(5) | |
| for value, pred in zip(values, predictions): | |
| print(f"{tokenizer.decode([pred])} with confidence {value}") | |
| ``` |