# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("TunahanGokcimen/albert-base-v2-cased-ner")
model = AutoModelForTokenClassification.from_pretrained("TunahanGokcimen/albert-base-v2-cased-ner", device_map="auto")Quick Links
albert-base-v2-cased-ner
This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1912
- Precision: 0.7496
- Recall: 0.8064
- F1: 0.7770
- Accuracy: 0.9384
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.225 | 1.0 | 2078 | 0.2281 | 0.6778 | 0.7389 | 0.7070 | 0.9224 |
| 0.1849 | 2.0 | 4156 | 0.1909 | 0.7194 | 0.8032 | 0.7590 | 0.9360 |
| 0.1379 | 3.0 | 6234 | 0.1912 | 0.7496 | 0.8064 | 0.7770 | 0.9384 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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Model tree for TunahanGokcimen/albert-base-v2-cased-ner
Base model
albert/albert-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TunahanGokcimen/albert-base-v2-cased-ner")