Improving Black-box Robustness with In-Context Rewriting
Collection
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How to use Kyle1668/boss-sentiment-3000-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Kyle1668/boss-sentiment-3000-bert-base-uncased") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Kyle1668/boss-sentiment-3000-bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("Kyle1668/boss-sentiment-3000-bert-base-uncased")This model is a fine-tuned version of bert-base-uncased on the None 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 | F1 | Acc | Validation Loss |
|---|---|---|---|---|---|
| No log | 1.0 | 188 | 0.5948 | 0.8309 | 0.5611 |
| No log | 2.0 | 376 | 0.6500 | 0.7876 | 0.5856 |
| 0.6906 | 3.0 | 564 | 0.7083 | 0.8627 | 0.3858 |
| 0.6906 | 4.0 | 752 | 0.6588 | 0.7697 | 0.8185 |
| 0.6906 | 5.0 | 940 | 0.6687 | 0.8142 | 0.8388 |
| 0.1956 | 6.0 | 1128 | 0.6539 | 0.8012 | 1.0668 |
Base model
google-bert/bert-base-uncased