From 24688b3f54647555d0f793315faa33205d1c573d Mon Sep 17 00:00:00 2001 From: Moritz Laurer Date: Sun, 5 Dec 2021 16:55:14 +0000 Subject: [PATCH] Update README.md --- README.md | 20 +++++--------------- 1 file changed, 5 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 29c656f..0bdaa74 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ ---- - --- language: - multilingual @@ -14,18 +12,10 @@ metrics: datasets: - mnli - xnli -- anli -license: mit pipeline_tag: zero-shot-classification widget: -- text: "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus." - candidate_labels: "violent, peaceful" -- text: "La película empezaba bien pero terminó siendo un desastre." - candidate_labels: "positivo, negativo, neutral" -- text: "La película empezó siendo un desastre pero en general fue bien." - candidate_labels: "positivo, negativo, neutral" -- text: "¿A quién vas a votar en 2020?" - candidate_labels: "Europa, elecciones, política, ciencia, deportes" +- text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" + candidate_labels: "politics, economy, entertainment, environment" --- # Multilingual mDeBERTa-v3-base-mnli-xnli ## Model description @@ -41,8 +31,8 @@ import torch model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-mnli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) -premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." -hypothesis = "The movie was good." +premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" +hypothesis = "Emmanuel Macron is the President of France" input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() @@ -70,7 +60,7 @@ training_args = TrainingArguments( The model was evaluated using the matched test set and achieves 0.90 accuracy. average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vu | zh ----------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|---------- +---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|---------- 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 {'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}