metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
  - sentiment_analysis
datasets:
  - ckandemir/bitcoin_tweets_sentiment_kaggle
metrics:
  - accuracy
  - f1
model-index:
  - name: bitcoin_tweet_sentiment_classification
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: ckandemir/bitcoin_tweets_sentiment_kaggle
          type: ckandemir/bitcoin_tweets_sentiment_kaggle
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7150837988826816
          - name: F1
            type: f1
            value: 0.7212944928862212
language:
  - en
library_name: transformers
widget:
  - text: Sold all btc, tethered up before the correction.
pipeline_tag: token-classification

bitcoin_tweet_sentiment_classification

This model is a fine-tuned version of bert-base-uncased on the ckandemir/bitcoin_tweets_sentiment_kaggle dataset. It achieves the following results on the evaluation set:

Loss: 0.4542
Accuracy: 0.7151
F1: 0.7213

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: 5e-06
train_batch_size: 24
eval_batch_size: 24
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 72
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 1000
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.8941	0.65	50	0.8733	0.5698	0.5654
0.8565	1.3	100	0.8042	0.6690	0.6031
0.7896	1.96	150	0.7219	0.6802	0.5740
0.7174	2.61	200	0.6379	0.7514	0.6955
0.633	3.26	250	0.5745	0.7514	0.6930
0.5824	3.91	300	0.5303	0.75	0.6919
0.5365	4.57	350	0.4997	0.7514	0.7014
0.5089	5.22	400	0.4766	0.7458	0.6991
0.4893	5.87	450	0.4596	0.7486	0.7174
0.463	6.52	500	0.4446	0.7514	0.7127
0.4496	7.17	550	0.4407	0.7165	0.7048
0.4357	7.83	600	0.4364	0.7277	0.7246
0.4257	8.48	650	0.4324	0.7067	0.7115
0.4029	9.13	700	0.4314	0.7277	0.7180
0.3955	9.78	750	0.4354	0.7151	0.7164
0.3886	10.43	800	0.4396	0.7221	0.7244
0.3788	11.09	850	0.4363	0.7235	0.7194
0.366	11.74	900	0.4528	0.7179	0.7215
0.3298	12.39	950	0.4766	0.7053	0.7107
0.3423	13.04	1000	0.4542	0.7151	0.7213

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1