As many of the donors in the training dataset have mixed up Adaptation and Mitigation, the model's ability to differentiate the two isn't perfect. But the sigmoid of the model logits do bias toward the correct class.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-06
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.7689	1.0	1951	0.7993	0.6421	0.6477	0.5264	0.8230
0.6217	2.0	3902	0.8303	0.6737	0.6269	0.5814	0.8010
0.5834	3.0	5853	0.8266	0.6761	0.6101	0.5715	0.8276
0.5571	4.0	7804	0.8461	0.6933	0.6169	0.6144	0.7954
0.5323	5.0	9755	0.8366	0.6869	0.6050	0.5913	0.8194
0.5126	6.0	11706	0.8327	0.6867	0.6047	0.5815	0.8385
0.4968	7.0	13657	0.8408	0.6938	0.6098	0.5989	0.8244
0.4893	8.0	15608	0.6040	0.8348	0.6895	0.5854	0.8387
0.4702	9.0	17559	0.6342	0.8508	0.7050	0.6211	0.8151
0.4514	10.0	19510	0.6210	0.8383	0.6946	0.5918	0.8404
0.4323	11.0	21461	0.6340	0.8402	0.6991	0.5943	0.8487
0.4193	12.0	23412	0.6407	0.8433	0.7005	0.6020	0.8375
0.407	13.0	25363	0.6602	0.8526	0.7094	0.6237	0.8223
0.3944	14.0	27314	0.6588	0.8441	0.7026	0.6029	0.8419
0.3834	15.0	29265	0.6881	0.8529	0.7110	0.6233	0.8274
0.3738	16.0	31216	0.7029	0.8575	0.7146	0.6359	0.8155
0.3686	17.0	33167	0.6929	0.8524	0.7102	0.6224	0.8271
0.3607	18.0	35118	0.7069	0.8545	0.7127	0.6272	0.8253
0.3556	19.0	37069	0.7072	0.8543	0.7118	0.6274	0.8225
0.3523	20.0	39020	0.7080	0.8541	0.7121	0.6265	0.8248

Framework versions

Transformers 4.41.0
Pytorch 2.0.1
Datasets 2.19.1
Tokenizers 0.19.1

alex-miller/iati-climate-multi-classifier-weighted2

iati-climate-multi-classifier-weighted2

Model description

Intended uses & limitations

Training hyperparameters

Training results

Framework versions

alex-miller/
iati-climate-multi-classifier-weighted2