leondz/wnut_17
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How to use Gladiator/funnel-transformer-xlarge_ner_wnut_17 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="Gladiator/funnel-transformer-xlarge_ner_wnut_17") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Gladiator/funnel-transformer-xlarge_ner_wnut_17")
model = AutoModelForTokenClassification.from_pretrained("Gladiator/funnel-transformer-xlarge_ner_wnut_17", device_map="auto")This model is a fine-tuned version of funnel-transformer/xlarge 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 | 0.2331 | 0.6897 | 0.4067 | 0.5117 | 0.9462 |
| No log | 2.0 | 426 | 0.2056 | 0.7097 | 0.5526 | 0.6214 | 0.9587 |
| 0.1454 | 3.0 | 639 | 0.2379 | 0.7102 | 0.5658 | 0.6298 | 0.9600 |
| 0.1454 | 4.0 | 852 | 0.2397 | 0.7141 | 0.5885 | 0.6452 | 0.9620 |
| 0.0319 | 5.0 | 1065 | 0.2453 | 0.7205 | 0.5921 | 0.6500 | 0.9620 |