leondz/wnut_17
Updated • 4.13k • 19
How to use juannmy400/my_awesome_wnut_model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="juannmy400/my_awesome_wnut_model") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("juannmy400/my_awesome_wnut_model")
model = AutoModelForTokenClassification.from_pretrained("juannmy400/my_awesome_wnut_model")This model is a fine-tuned version of juannmy400/my_awesome_wnut_model on the wnut_17 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2823 | 0.6334 | 0.3058 | 0.4125 | 0.9438 |
| No log | 2.0 | 426 | 0.2819 | 0.5808 | 0.3596 | 0.4442 | 0.9458 |
| 0.0792 | 3.0 | 639 | 0.2933 | 0.5256 | 0.3902 | 0.4479 | 0.9457 |
| 0.0792 | 4.0 | 852 | 0.3066 | 0.5080 | 0.4115 | 0.4547 | 0.9457 |
| 0.0295 | 5.0 | 1065 | 0.3095 | 0.5081 | 0.4059 | 0.4513 | 0.9448 |
| 0.0295 | 6.0 | 1278 | 0.3382 | 0.5341 | 0.4208 | 0.4707 | 0.9463 |
| 0.0295 | 7.0 | 1491 | 0.3547 | 0.5475 | 0.4115 | 0.4698 | 0.9469 |
| 0.0146 | 8.0 | 1704 | 0.3703 | 0.5645 | 0.3892 | 0.4608 | 0.9465 |
| 0.0146 | 9.0 | 1917 | 0.3635 | 0.5672 | 0.4106 | 0.4763 | 0.9472 |
| 0.0082 | 10.0 | 2130 | 0.3680 | 0.5727 | 0.4050 | 0.4745 | 0.9472 |
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