Auc roc curve

NLP.py

@@ -0,0 +1,16 @@
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+print("Hello world")
+pipe = pipeline("translation", model="facebook/nllb-200-distilled-600M")
+
+from transformers import AutoTokenizer, M2M100ForConditionalGeneration
+
+model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
+
+text_to_translate = "Life is like a box of chocolates"
+model_inputs = tokenizer(text_to_translate, return_tensors="pt")
+
+# translate to French
+gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("fr"))
+print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))

requirements.txt

@@ -1,4 +1,8 @@
-matplotlib==3.8.3
+lightgbm==4.3.0
 pandas==1.5.3
+scikit_learn==1.2.1
 scikit_learn==1.4.1.post1
 streamlit==1.32.0
+transformers==4.24.0
+transformers==4.39.2
+xgboost==2.0.3