CryptoBERT
This model is a fine-tuned version of ProsusAI/finbert on the Custom Crypto Market Sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 0.3823
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
tokenizer = BertTokenizer.from_pretrained("kk08/CryptoBERT")
model = BertForSequenceClassification.from_pretrained("kk08/CryptoBERT")
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = "Bitcoin (BTC) touches $29k, Ethereum (ETH) Set To Explode, RenQ Finance (RENQ) Crosses Massive Milestone"
result = classifier(text)
print(result)
[{'label': 'LABEL_1', 'score': 0.9678454399108887}]
Model description
This model fine-tunes the ProsusAI/finbert, which is a pre-trained NLP model to analyze the sentiment of the financial text. CryptoBERT model fine-tunes this by training the model as a downstream task on Custom Crypto Sentiment data to predict whether the given text related to the Crypto market is Positive (LABEL_1) or Negative (LABEL_0).
Intended uses & limitations
The model can perform well on Crypto-related data. The main limitation is that the fine-tuning was done using only a small corpus of data
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4077 | 1.0 | 27 | 0.4257 |
| 0.2048 | 2.0 | 54 | 0.2479 |
| 0.0725 | 3.0 | 81 | 0.3068 |
| 0.0028 | 4.0 | 108 | 0.4120 |
| 0.0014 | 5.0 | 135 | 0.3566 |
| 0.0007 | 6.0 | 162 | 0.3495 |
| 0.0006 | 7.0 | 189 | 0.3645 |
| 0.0005 | 8.0 | 216 | 0.3754 |
| 0.0004 | 9.0 | 243 | 0.3804 |
| 0.0004 | 10.0 | 270 | 0.3823 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3