# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("andi611/roberta-base-ner-conll2003")
model = AutoModelForTokenClassification.from_pretrained("andi611/roberta-base-ner-conll2003", device_map="auto")Quick Links
roberta-base-ner
This model is a fine-tuned version of roberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315.646
- eval_steps_per_second: 39.529
- epoch: 1.14
- step: 500
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="andi611/roberta-base-ner-conll2003")