Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use NathanJLee/NLP2_Base_3e-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NathanJLee/NLP2_Base_3e-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NathanJLee/NLP2_Base_3e-4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NathanJLee/NLP2_Base_3e-4") model = AutoModelForSequenceClassification.from_pretrained("NathanJLee/NLP2_Base_3e-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
NLP2_Base_3e-4
This model is a fine-tuned version of NathanJLee/NLP2_Base_1e-4 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
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Model tree for NathanJLee/NLP2_Base_3e-4
Base model
distilbert/distilbert-base-uncased Finetuned
NathanJLee/NLP2_Base_1e-4