cardiffnlp/tweet_eval
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How to use aXhyra/sentiment_trained_31415 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/sentiment_trained_31415") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/sentiment_trained_31415")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/sentiment_trained_31415")This model is a fine-tuned version of distilbert-base-uncased 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 | F1 |
|---|---|---|---|---|
| 0.651 | 1.0 | 11404 | 0.6669 | 0.7141 |
| 0.6066 | 2.0 | 22808 | 0.8160 | 0.7198 |
| 0.503 | 3.0 | 34212 | 1.0659 | 0.7182 |
| 0.386 | 4.0 | 45616 | 1.2481 | 0.7188 |