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Rahul7979/
MoodPredictor

sentence-transformers/all-mpnet-base-v2

finetune

apache-2.0

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Then commit and push the updated README.

2. Check That `model.safetensors` Was Detected

Go to the “Files and versions” tab in your Hugging Face repo. If it shows model.safetensors (even with LFS), you're good to go.

3. (Optional) Add `config.json` and `tokenizer` files

If you want to load the model easily using pipeline(), Hugging Face expects these files:

config.json
tokenizer_config.json
tokenizer.json
vocab.txt or similar (depends on tokenizer)

If you trained your model using Trainer or saved it with model.save_pretrained("folder"), those files should already be there. If not, let me know and I’ll guide you to regenerate them.

4. Test Inference (Optional but Useful)

If you want, you can now try loading the model like this:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model = AutoModelForSequenceClassification.from_pretrained("Rahul7979/MoodPredictor")
tokenizer = AutoTokenizer.from_pretrained("Rahul7979/MoodPredictor")

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
print(classifier("I love how this works!"))

Rahul7979/MoodPredictor

2. Check That model.safetensors Was Detected

3. (Optional) Add config.json and tokenizer files

4. Test Inference (Optional but Useful)

Rahul7979/
MoodPredictor

2. Check That `model.safetensors` Was Detected

3. (Optional) Add `config.json` and `tokenizer` files