cardiffnlp/tweet_eval
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How to use muhtasham/base-vanilla-target-tweet with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="muhtasham/base-vanilla-target-tweet") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("muhtasham/base-vanilla-target-tweet")
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/base-vanilla-target-tweet")This model is a fine-tuned version of google/bert_uncased_L-12_H-768_A-12 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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.3831 | 4.9 | 500 | 0.9800 | 0.7807 | 0.7785 |
| 0.0414 | 9.8 | 1000 | 1.4175 | 0.7754 | 0.7765 |
| 0.015 | 14.71 | 1500 | 1.6411 | 0.7754 | 0.7708 |
| 0.0166 | 19.61 | 2000 | 1.5930 | 0.7941 | 0.7938 |
| 0.0175 | 24.51 | 2500 | 1.3934 | 0.7888 | 0.7852 |
| 0.0191 | 29.41 | 3000 | 1.9407 | 0.7647 | 0.7658 |
| 0.0137 | 34.31 | 3500 | 1.8380 | 0.7781 | 0.7773 |