# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("adasgaleus/insertbert05")
model = AutoModelForTokenClassification.from_pretrained("adasgaleus/insertbert05")Quick Links
insertbert05
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1928
- Precision: 0.8055
- Recall: 0.7865
- F1: 0.7959
- Accuracy: 0.9189
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2897 | 0.32 | 500 | 0.2188 | 0.7808 | 0.7490 | 0.7646 | 0.9065 |
| 0.2218 | 0.64 | 1000 | 0.1988 | 0.8002 | 0.7774 | 0.7887 | 0.9161 |
| 0.2103 | 0.96 | 1500 | 0.1928 | 0.8055 | 0.7865 | 0.7959 | 0.9189 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="adasgaleus/insertbert05")