Instructions to use ychu612/RSAVAV_FN_CLF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychu612/RSAVAV_FN_CLF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ychu612/RSAVAV_FN_CLF")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychu612/RSAVAV_FN_CLF") model = AutoModelForSequenceClassification.from_pretrained("ychu612/RSAVAV_FN_CLF") - Notebooks
- Google Colab
- Kaggle
RSAVAV_FN_CLF
This model is a fine-tuned version of allenai/longformer-base-4096 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Downloads last month
- 5
Model tree for ychu612/RSAVAV_FN_CLF
Base model
allenai/longformer-base-4096