# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("s8n29/deberta_jb_classification_v1")
model = AutoModelForSequenceClassification.from_pretrained("s8n29/deberta_jb_classification_v1", device_map="auto")Quick Links
deberta_jb_classification_v1
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-emotion 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 439 | 0.0 | 1.0 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for s8n29/deberta_jb_classification_v1
Base model
cardiffnlp/twitter-roberta-base-emotion
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="s8n29/deberta_jb_classification_v1")