# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("devanshrj/roberta-ner")
model = AutoModelForTokenClassification.from_pretrained("devanshrj/roberta-ner")Quick Links
roberta-ner
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1963
- Precision: 0.3814
- Recall: 0.4134
- F1: 0.3968
- Accuracy: 0.9525
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 60 | 0.2553 | 0.1878 | 0.1075 | 0.1368 | 0.9435 |
| No log | 2.0 | 120 | 0.2114 | 0.3456 | 0.2235 | 0.2714 | 0.9492 |
| No log | 3.0 | 180 | 0.2007 | 0.3372 | 0.3673 | 0.3516 | 0.9494 |
| No log | 4.0 | 240 | 0.1976 | 0.3618 | 0.3911 | 0.3758 | 0.9517 |
| No log | 5.0 | 300 | 0.1963 | 0.3814 | 0.4134 | 0.3968 | 0.9525 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for devanshrj/roberta-ner
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devanshrj/roberta-ner")