| # load model | |
| ``` | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoConfig, | |
| AutoModelForSeq2SeqLM | |
| ) | |
| model_path = "T5-large-esnli-impli-figurative" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| config = AutoConfig.from_pretrained(model_path) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_path) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| premise = "I just caught a guy picking up used chewing gum and he put it in his mouth." | |
| hypothesis = "it was such a pleasant sight to see a guy picking up used chewing gum; and he put it in his mouth" | |
| prepared_input = f"figurative hypothesis: {hypothesis} premise: {premise}" | |
| features = tokenizer(prepared_input, max_length=128, padding="max_length", truncation=True, return_tensors="pt") | |
| model.eval() | |
| model.to(device) | |
| with torch.no_grad(): | |
| # https://huggingface.co/blog/how-to-generate | |
| generated_ids = model.generate( | |
| **features, | |
| max_length=128, | |
| use_cache=True, | |
| num_beams=4, | |
| length_penalty=0.6, | |
| early_stopping=True, | |
| ) | |
| dec_preds = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print("The prediction is: ", dec_preds) | |
| print(dec_preds[1:].replace("explanation:", "").lstrip()) | |
| ``` | |
| # Example input | |
| figurative hypothesis: I was gone for only a few days and my considerate adult son just let the sink fill up with dirty dishes, making me feel really happy premise: I left my adult son home for a few days and just came back to a sink full of gross old dishes. |