span-marker-robert-base

This model is a fine-tuned version of roberta-base on few-nerd dataset using SpanMarker an module for NER.

Usage

  from span_marker import SpanMarkerModel
  
  model = SpanMarkerModel.from_pretrained("krinal/span-marker-robert-base")
  
  ner_result = model.predict("Argentine captain Lionel Messi won Golden Ball at FIFA world cup 2022")

Training and evaluation data

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Evaluation

It achieves the following results on the evaluation set:

• Loss: 0.0214
• Overall Precision: 0.7642
• Overall Recall: 0.7947
• Overall F1: 0.7791
• Overall Accuracy: 0.9397

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0214	0.08	100	0.0219	0.7641	0.7679	0.7660	0.9330
0.0199	0.16	200	0.0243	0.7442	0.7679	0.7559	0.9348
0.0179	0.24	300	0.0212	0.7730	0.7580	0.7654	0.9361
0.0188	0.33	400	0.0225	0.7616	0.7710	0.7662	0.9343
0.0149	0.41	500	0.0240	0.7537	0.7783	0.7658	0.9375
0.015	0.49	600	0.0230	0.7540	0.7829	0.7682	0.9362
0.0137	0.57	700	0.0232	0.7746	0.7538	0.7640	0.9319
0.0123	0.65	800	0.0218	0.7651	0.7879	0.7763	0.9393
0.0103	0.73	900	0.0223	0.7688	0.7964	0.7824	0.9397
0.0108	0.82	1000	0.0209	0.7763	0.7816	0.7789	0.9397
0.0116	0.9	1100	0.0213	0.7743	0.7879	0.7811	0.9398
0.0119	0.98	1200	0.0214	0.7653	0.7947	0.7797	0.9400

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3
• span-marker 1.2.3