Classifier Prompted
Collection
Classifier retrained with prompt used during project • 3 items • Updated
How to use PracticalWork/ModernBERT-large-classifier-prompted with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="PracticalWork/ModernBERT-large-classifier-prompted") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("PracticalWork/ModernBERT-large-classifier-prompted")
model = AutoModelForSequenceClassification.from_pretrained("PracticalWork/ModernBERT-large-classifier-prompted")This model is a fine-tuned version of PracticalWork/ModernBERT-large-classifier on an unknown 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 |
|---|---|---|---|---|---|
| No log | 0 | 0 | 1.9514 | 0.6935 | 0.5679 |
| No log | 0.6 | 255 | 0.2441 | 0.9037 | 0.8982 |
| 0.2289 | 1.2 | 510 | 0.3278 | 0.9019 | 0.8994 |
| 0.2289 | 1.8 | 765 | 0.3406 | 0.9037 | 0.9018 |
| 0.0899 | 2.4 | 1020 | 0.4322 | 0.9019 | 0.8973 |
| 0.0899 | 3.0 | 1275 | 0.4295 | 0.9054 | 0.9015 |
Base model
answerdotai/ModernBERT-large