Instructions to use CoCoRooXin/test_eu_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CoCoRooXin/test_eu_adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("facebook/bart-large-mnli") model = PeftModel.from_pretrained(base_model, "CoCoRooXin/test_eu_adapter") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from CoCoRooXin/test_eu_adapter: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/CoCoRooXin/test_eu_adapter/resolve/main/README.md
- Command line
-
hf download hf://CoCoRooXin/test_eu_adapter/README.md
-
curl -L -o README.md https://huggingface.co/CoCoRooXin/test_eu_adapter/resolve/main/README.md
1.14 kB
metadata
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/bart-large-mnli
model-index:
- name: test_eu_adapter
results: []
test_eu_adapter
This model is a fine-tuned version of facebook/bart-large-mnli on an unknown 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 2
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1