Instructions to use jaggernaut007/roberta-large-finetuned-abbr-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaggernaut007/roberta-large-finetuned-abbr-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jaggernaut007/roberta-large-finetuned-abbr-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jaggernaut007/roberta-large-finetuned-abbr-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jaggernaut007/roberta-large-finetuned-abbr-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-large-finetuned-abbr-finetuned-ner
This model is a fine-tuned version of surrey-nlp/roberta-large-finetuned-abbr on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4909
- Precision: 0.8918
- Recall: 0.8917
- F1: 0.8917
- Accuracy: 0.8839
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: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
- Downloads last month
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Model tree for jaggernaut007/roberta-large-finetuned-abbr-finetuned-ner
Base model
FacebookAI/roberta-large Finetuned
surrey-nlp/roberta-large-finetuned-abbr