cardiffnlp/tweet_eval
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How to use muhtasham/small-vanilla-target-tweet with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="muhtasham/small-vanilla-target-tweet") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("muhtasham/small-vanilla-target-tweet")
model = AutoModelForSequenceClassification.from_pretrained("muhtasham/small-vanilla-target-tweet", device_map="auto")This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 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 |
|---|---|---|---|---|---|
| 0.5858 | 4.9 | 500 | 0.8189 | 0.7380 | 0.7364 |
| 0.1039 | 9.8 | 1000 | 1.1965 | 0.7594 | 0.7568 |
| 0.0264 | 14.71 | 1500 | 1.5387 | 0.7433 | 0.7460 |
| 0.0142 | 19.61 | 2000 | 1.6758 | 0.7620 | 0.7551 |
| 0.0113 | 24.51 | 2500 | 1.8718 | 0.7540 | 0.7525 |