roberta-large-finetuned-ner

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roberta-large-finetuned-ner

Model description

roberta-large-finetuned-ner is a fine-tuned Roberta model that is ready to use for Named Entity Recognition. It has been trained to recognize eight types of entities: Geographical, Organization, Person, Geopolitical Entity, Time indicator, Artifact, Event, Natural Phenomenon. This model is a fine-tuned version of FacebookAI/roberta-large on an Named Entity Recognition (NER) Corpus dataset.

It achieves the following results on the evaluation set:

• Train Loss: 0.1164
• Validation Loss: 0.0878
• Train Precision: 0.8442
• Train Recall: 0.8358
• Train F1: 0.8400
• Train Accuracy: 0.9718
• Epoch: 0

Intended uses & limitations

How to use:

You can use this model with Transformers pipeline for NER.

 from transformers import AutoTokenizer, TFAutoModelForTokenClassification
 from transformers import pipeline

 tokenizer = AutoTokenizer.from_pretrained("Astral7/roberta-large-finetuned-ner",add_prefix_space=True)
 model = TFAutoModelForTokenClassification.from_pretrained("Astral7/roberta-large-finetuned-ner")
 
 nlp_pipe = pipeline("token-classification", model=model,tokenizer=tokenizer )
 example = "My name is Clara and I live in Berkeley, California."

 results=nlp_pipe(example)
 print(results)

Limitations:

This model is limited by its training dataset of Annotated Corpus for Named Entity Recognition is annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.

Training and evaluation data

This model was fine-tuned on Corpus for Named Entity Recognition Dataset.

As in the dataset, each token will be classified as one of the following classes:

Training procedure

This model was trained on a single T4 GPU.

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: { "name": "AdamWeightDecay", "learning_rate": { "module": "keras.optimizers.schedules", "class_name": "PolynomialDecay", "config": { "initial_learning_rate": 2e-05, "decay_steps": 4795, "end_learning_rate": 0.0, "power": 1.0, "cycle": False, "name": None, }, "registered_name": None, }, "decay": 0.0, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-08, "amsgrad": False, "weight_decay_rate": 0.01, } -
• training_precision: float32

Training results

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.17.1
• Tokenizers 0.15.2