cardiffnlp/tweet_eval
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How to use siberett/roberta-sentiment-analysis-finetune with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="siberett/roberta-sentiment-analysis-finetune") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("siberett/roberta-sentiment-analysis-finetune")
model = AutoModelForSequenceClassification.from_pretrained("siberett/roberta-sentiment-analysis-finetune")This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on the tweet_eval dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5451 | 1.0 | 713 | 0.5422 |
| 0.4785 | 2.0 | 1426 | 0.5585 |
| 0.4199 | 3.0 | 2139 | 0.5785 |
| 0.3608 | 4.0 | 2852 | 0.6038 |
| 0.3117 | 5.0 | 3565 | 0.6713 |
| 0.2684 | 6.0 | 4278 | 0.7366 |
| 0.2403 | 7.0 | 4991 | 0.7737 |
| 0.2137 | 8.0 | 5704 | 0.8276 |
| 0.1926 | 9.0 | 6417 | 0.8597 |
| 0.1778 | 10.0 | 7130 | 0.8863 |