Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Jamvess/results")
model = AutoModelForSequenceClassification.from_pretrained("Jamvess/results")Quick Links
results
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
Training results
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
- Downloads last month
- 4
Model tree for Jamvess/results
Base model
cardiffnlp/twitter-roberta-base-sentiment
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jamvess/results")