Update README.md
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README.md
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README.md
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---
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---
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---
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language:
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language:
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- multilingual
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- multilingual
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datasets:
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datasets:
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- mnli
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- mnli
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- xnli
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- xnli
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- anli
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license: mit
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pipeline_tag: zero-shot-classification
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pipeline_tag: zero-shot-classification
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widget:
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widget:
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- text: "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus."
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- text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
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candidate_labels: "violent, peaceful"
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candidate_labels: "politics, economy, entertainment, environment"
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- text: "La película empezaba bien pero terminó siendo un desastre."
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candidate_labels: "positivo, negativo, neutral"
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- text: "La película empezó siendo un desastre pero en general fue bien."
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candidate_labels: "positivo, negativo, neutral"
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- text: "¿A quién vas a votar en 2020?"
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candidate_labels: "Europa, elecciones, política, ciencia, deportes"
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---
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---
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# Multilingual mDeBERTa-v3-base-mnli-xnli
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# Multilingual mDeBERTa-v3-base-mnli-xnli
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## Model description
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## Model description
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@ -41,8 +31,8 @@ import torch
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model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-mnli"
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model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-mnli"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
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premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
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hypothesis = "The movie was good."
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hypothesis = "Emmanuel Macron is the President of France"
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input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
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input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
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output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
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output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
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prediction = torch.softmax(output["logits"][0], -1).tolist()
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prediction = torch.softmax(output["logits"][0], -1).tolist()
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The model was evaluated using the matched test set and achieves 0.90 accuracy.
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The model was evaluated using the matched test set and achieves 0.90 accuracy.
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average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vu | zh
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average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vu | zh
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---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------
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---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------
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0.808 | 0.802 | 0.829 | 0.825 | 0.826 | 0.883 | 0.845 | 0.834 | 0.771 | 0.813 | 0.748 | 0.793 | 0.807 | 0.740 | 0.795 | 0.8116
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0.808 | 0.802 | 0.829 | 0.825 | 0.826 | 0.883 | 0.845 | 0.834 | 0.771 | 0.813 | 0.748 | 0.793 | 0.807 | 0.740 | 0.795 | 0.8116
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{'ar': 0.8017964071856287, 'bg': 0.8287425149700599, 'de': 0.8253493013972056, 'el': 0.8267465069860279, 'en': 0.8830339321357286, 'es': 0.8449101796407186, 'fr': 0.8343313373253493, 'hi': 0.7712574850299401, 'ru': 0.8127744510978044, 'sw': 0.7483033932135729, 'th': 0.792814371257485, 'tr': 0.8065868263473054, 'ur': 0.7403193612774451, 'vi': 0.7954091816367266, 'zh': 0.8115768463073852}
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{'ar': 0.8017964071856287, 'bg': 0.8287425149700599, 'de': 0.8253493013972056, 'el': 0.8267465069860279, 'en': 0.8830339321357286, 'es': 0.8449101796407186, 'fr': 0.8343313373253493, 'hi': 0.7712574850299401, 'ru': 0.8127744510978044, 'sw': 0.7483033932135729, 'th': 0.792814371257485, 'tr': 0.8065868263473054, 'ur': 0.7403193612774451, 'vi': 0.7954091816367266, 'zh': 0.8115768463073852}
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