stashed changes

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+-----END CERTIFICATE-----

pipelines.py

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+# from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+# import torch
+# tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+# model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+# inputs = tokenizer("Hello, my dog is sad", return_tensors="pt")
+
+
+# inputs = tokenizer("Hello, my dog is sad", return_tensors="pt")
+# with torch.no_grad():
+#     logits = model(**inputs).logits
+
+
+
+# predicted_class_id = logits.argmax().item()
+
+# model.config.id2label[predicted_class_id]
+
+
+# outputs = model(**inputs)
+
+# print(predicted_class_id)
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+tokenizer.pad_token = tokenizer.eos_token
+# Load the model
+model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+
+# Example input prompt
+input_text = "Ann wants to buy a new car"
+
+# Tokenize input
+
+inputs = tokenizer(input_text, return_tensors="pt",padding=True, truncation=True)
+
+# Generate text
+outputs = model.generate(inputs.input_ids, max_length=100, num_return_sequences=1, top_k=50, top_p=0.9, temperature=0.7,do_sample=True,eos_token_id=None, attention_mask=inputs.attention_mask)
+print(model.config)
+# Decode the generated text
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print("Generated Text:\n", generated_text)

training.py

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+from huggingface_hub import HfApi
+
+HfApi().create_repo(repo_id="annetade/cv_fit", repo_type="dataset")
+
+
+
+
+
+
+
+
+# splits = {'train': 'train.csv', 'test': 'test.csv'}
+# df = pd.read_csv("hf://datasets/cnamuangtoun/resume-job-description-fit/" + splits["train"])