metadata

license: mit
base_model: cointegrated/rubert-tiny2
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: rubert-tiny2-odonata-extended-305-1-ner
    results: []

rubert-tiny2-odonata-extended-305-1-ner

This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0101
Precision: 0.6420
Recall: 0.5821
F1: 0.6106
Accuracy: 0.9967

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	32	0.1033	0.0	0.0	0.0	0.9952
No log	2.0	64	0.0391	0.0	0.0	0.0	0.9952
No log	3.0	96	0.0351	0.0	0.0	0.0	0.9952
No log	4.0	128	0.0321	0.0	0.0	0.0	0.9952
No log	5.0	160	0.0260	0.0	0.0	0.0	0.9952
No log	6.0	192	0.0188	0.6809	0.1194	0.2032	0.9955
No log	7.0	224	0.0158	0.6480	0.4328	0.5190	0.9961
No log	8.0	256	0.0143	0.6567	0.4925	0.5629	0.9964
No log	9.0	288	0.0133	0.6573	0.4366	0.5247	0.9963
No log	10.0	320	0.0127	0.5898	0.5634	0.5763	0.9964
No log	11.0	352	0.0122	0.6128	0.5373	0.5726	0.9965
No log	12.0	384	0.0119	0.6122	0.6007	0.6064	0.9965
No log	13.0	416	0.0114	0.6295	0.5261	0.5732	0.9965
No log	14.0	448	0.0112	0.6349	0.5709	0.6012	0.9967
No log	15.0	480	0.0111	0.6174	0.6082	0.6128	0.9966
0.0665	16.0	512	0.0108	0.6491	0.5522	0.5968	0.9967
0.0665	17.0	544	0.0108	0.6232	0.6418	0.6324	0.9967
0.0665	18.0	576	0.0106	0.6571	0.5149	0.5774	0.9967
0.0665	19.0	608	0.0105	0.6271	0.5522	0.5873	0.9965
0.0665	20.0	640	0.0105	0.6332	0.6119	0.6224	0.9967
0.0665	21.0	672	0.0104	0.6390	0.5746	0.6051	0.9966
0.0665	22.0	704	0.0104	0.6316	0.5821	0.6058	0.9966
0.0665	23.0	736	0.0103	0.6444	0.5410	0.5882	0.9966
0.0665	24.0	768	0.0103	0.6287	0.5560	0.5901	0.9966
0.0665	25.0	800	0.0102	0.6322	0.5709	0.6000	0.9966
0.0665	26.0	832	0.0102	0.6360	0.5672	0.5996	0.9966
0.0665	27.0	864	0.0102	0.6352	0.5784	0.6055	0.9966
0.0665	28.0	896	0.0102	0.6453	0.5634	0.6016	0.9967
0.0665	29.0	928	0.0101	0.6402	0.5709	0.6036	0.9967
0.0665	30.0	960	0.0101	0.6420	0.5821	0.6106	0.9967

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cpu
Datasets 2.19.2
Tokenizers 0.19.1