# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nateraw/codecarbon-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("nateraw/codecarbon-text-classification")Quick Links
codecarbon-text-classification
This model is a fine-tuned version of bert-base-cased on the imdb 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: 5e-05
- 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: 4
Training results
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nateraw/codecarbon-text-classification")