# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("rahmanansari/Adam-NER-Model")
model = AutoModelForTokenClassification.from_pretrained("rahmanansari/Adam-NER-Model")Quick Links
bert-finetuned-ner-adam
This model is a fine-tuned version of dslim/bert-large-NER on an hyperhustle/ner-dataset dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8845
- Recall: 0.8749
- F1: 0.8797
- Accuracy: 0.9646
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 |
|---|---|---|---|---|---|---|---|
| 0.0949 | 1.0 | 3080 | nan | 0.8914 | 0.8942 | 0.8928 | 0.9663 |
| 0.0574 | 2.0 | 6160 | nan | 0.8763 | 0.8784 | 0.8773 | 0.9635 |
| 0.0376 | 3.0 | 9240 | nan | 0.8845 | 0.8749 | 0.8797 | 0.9646 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for rahmanansari/Adam-NER-Model
Base model
dslim/bert-large-NER
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rahmanansari/Adam-NER-Model")