distilbert-base-uncased-finetuned-TT2-exam

roschmid/distilbert-base-uncased-finetuned-TT2-exam

distilbert-base-uncased-finetuned-TT2-exam is a token classification model from roschmid. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

1% of all-time downloads

All-time downloads

660

Public

Repo size

797 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin266 MB · 100%

At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: May 23, 2022
Updated: May 23, 2022
SHA: 0118ca0b

Try a prompt

Datasets

conll2003

Model card

From the Hugging Face model README

distilbert-base-uncased-finetuned-TT2-exam

This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.0620
Precision: 0.9222
Recall: 0.9369
F1: 0.9295
Accuracy: 0.9835

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2509	1.0	879	0.0733	0.8855	0.9212	0.9030	0.9777
0.0505	2.0	1758	0.0618	0.9221	0.9330	0.9275	0.9827
0.0309	3.0	2637	0.0620	0.9222	0.9369	0.9295	0.9835

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: May 23, 2022
Updated: May 23, 2022