# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gunkaynar/twitter-roberta-base-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("gunkaynar/twitter-roberta-base-sentiment")Quick Links
twitter-roberta-base-sentiment
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5590
- Accuracy: 0.6819
- F1: 0.7205
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: 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: 2
Training results
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.11.0
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Model tree for gunkaynar/twitter-roberta-base-sentiment
Base model
cardiffnlp/twitter-roberta-base-sentiment
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gunkaynar/twitter-roberta-base-sentiment")