cardiffnlp/tweet_eval
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How to use aXhyra/sentiment_trained_1234567 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="aXhyra/sentiment_trained_1234567") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aXhyra/sentiment_trained_1234567")
model = AutoModelForSequenceClassification.from_pretrained("aXhyra/sentiment_trained_1234567")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 |
|---|---|---|---|---|
| 0.6603 | 1.0 | 11404 | 0.7020 | 0.6992 |
| 0.5978 | 2.0 | 22808 | 0.8024 | 0.7151 |
| 0.5495 | 3.0 | 34212 | 1.0837 | 0.7139 |
| 0.4026 | 4.0 | 45616 | 1.2854 | 0.7165 |