metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - accuracy
model-index:
  - name: distilbert-finance
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: financial_phrasebank
          type: financial_phrasebank
          config: sentences_50agree
          split: train
          args: sentences_50agree
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7045454545454546

distilbert-finance

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

Loss: 1.7474
Accuracy: 0.7045

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: 0.0002
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6755	0.33	20	1.4948	0.3709
0.4585	0.66	40	0.9705	0.6147
0.4267	0.98	60	1.2383	0.6012
0.3298	1.31	80	1.0040	0.5764
0.2955	1.64	100	1.4078	0.4845
0.2521	1.97	120	1.2183	0.5702
0.1614	2.3	140	1.4761	0.6570
0.1842	2.62	160	1.8172	0.6002
0.2124	2.95	180	0.9596	0.7211
0.1016	3.28	200	1.3150	0.6952
0.0949	3.61	220	1.5779	0.6498
0.102	3.93	240	1.9178	0.5775
0.0542	4.26	260	2.0914	0.6074
0.059	4.59	280	1.7965	0.6560
0.0578	4.92	300	2.0358	0.5279
0.0335	5.25	320	1.5614	0.6829
0.0414	5.57	340	1.8126	0.6405
0.0263	5.9	360	1.4405	0.6798
0.0257	6.23	380	1.0230	0.7417
0.0123	6.56	400	1.9126	0.6818
0.0218	6.89	420	1.8622	0.6860
0.0063	7.21	440	2.0173	0.6705
0.014	7.54	460	1.9129	0.6870
0.0037	7.87	480	1.7622	0.7035
0.0155	8.2	500	1.7379	0.7004
0.0087	8.52	520	1.7150	0.6994
0.0055	8.85	540	1.7286	0.7025
0.0051	9.18	560	1.7418	0.7014
0.0049	9.51	580	1.7468	0.7035
0.0056	9.84	600	1.7474	0.7045

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3