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--- |
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inference: false |
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language: pt |
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datasets: |
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- assin2 |
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license: mit |
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--- |
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# DeBERTinha XSmall for Recognizing Textual Entailment |
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### **Labels**: |
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* 0 : There is no entailment between premise and hypothesis. |
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* 1 : There is entailment between premise and hypothesis. |
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## Full classification example |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig |
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import numpy as np |
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import torch |
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from scipy.special import softmax |
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model_name = "sagui-nlp/debertinha-ptbr-xsmall-assin2-rte" |
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s1 = "Os homens estão cuidadosamente colocando as malas no porta-malas de um carro." |
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s2 = "Os homens estão colocando bagagens dentro do porta-malas de um carro." |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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config = AutoConfig.from_pretrained(model_name) |
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model_input = tokenizer(*([s1], [s2]), padding=True, return_tensors="pt") |
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with torch.no_grad(): |
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output = model(**model_input) |
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scores = output[0][0].detach().numpy() |
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scores = softmax(scores) |
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ranking = np.argsort(scores) |
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ranking = ranking[::-1] |
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for i in range(scores.shape[0]): |
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l = config.id2label[ranking[i]] |
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s = scores[ranking[i]] |
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print(f"{i+1}) Label: {l} Score: {np.round(float(s), 4)}") |
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``` |
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## Citation |
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Comming soon |