Instructions to use spjabech/sentiment_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use spjabech/sentiment_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("gradientai/Llama-3-8B-Instruct-262k") model = PeftModel.from_pretrained(base_model, "spjabech/sentiment_classification") - Notebooks
- Google Colab
- Kaggle
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Download README.md from spjabech/sentiment_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
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https://huggingface.co/spjabech/sentiment_classification/resolve/main/README.md
- Command line
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hf download hf://spjabech/sentiment_classification/README.md
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curl -L -o README.md https://huggingface.co/spjabech/sentiment_classification/resolve/main/README.md
1.15 kB
metadata
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: gradientai/Llama-3-8B-Instruct-262k
model-index:
- name: sentiment_classification
results: []
sentiment_classification
This model is a fine-tuned version of gradientai/Llama-3-8B-Instruct-262k on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1