Update README.md

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}