README.md

metadata

base_model: roberta-large
license: mit
metrics:
  - accuracy
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: robertaL_ner
    results: []

robertaL_ner

This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2206
• Accuracy: 0.9558
• F1: 0.9558
• Precision: 0.9560
• Recall: 0.9558

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: 3e-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
• lr_scheduler_warmup_steps: 200
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.331	1.0	49	1.0308	0.6698	0.6131	0.6094	0.6698
0.8659	2.0	98	0.6391	0.7992	0.7929	0.7932	0.7992
0.5051	3.0	147	0.5164	0.8446	0.8389	0.8410	0.8446
0.4183	4.0	196	0.3752	0.8840	0.8827	0.8824	0.8840
0.4014	5.0	245	0.3487	0.8946	0.8926	0.8921	0.8946
0.2955	6.0	294	0.3009	0.9040	0.9049	0.9068	0.9040
0.2525	7.0	343	0.2478	0.9303	0.9303	0.9304	0.9303
0.2381	8.0	392	0.2498	0.9240	0.9243	0.9248	0.9240
0.2255	9.0	441	0.2214	0.9321	0.9318	0.9323	0.9321
0.1463	10.0	490	0.2258	0.9397	0.9396	0.9396	0.9397
0.151	11.0	539	0.2271	0.9421	0.9421	0.9422	0.9421
0.1213	12.0	588	0.2146	0.9500	0.9498	0.9499	0.9500
0.1166	13.0	637	0.2162	0.9494	0.9493	0.9496	0.9494
0.121	14.0	686	0.2442	0.9421	0.9424	0.9428	0.9421
0.0841	15.0	735	0.2206	0.9558	0.9558	0.9560	0.9558
0.0485	16.0	784	0.2555	0.9452	0.9452	0.9454	0.9452
0.0598	17.0	833	0.2338	0.9558	0.9558	0.9559	0.9558
0.0462	18.0	882	0.2443	0.9549	0.9549	0.9550	0.9549
0.0323	19.0	931	0.2531	0.9540	0.9540	0.9542	0.9540
0.0466	20.0	980	0.2509	0.9549	0.9549	0.9550	0.9549

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1