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# distilbert-base-uncased-sentiment-reddit-crypto

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the crypto-related reddit comments dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3070
- Accuracy: 0.8915

Accuracy on the final test set was: 0.8641

## Training and evaluation data

Training and validation data collected from 2 sources:

1. [Kaggle reddit cryptocurrency posts and comments](https://www.kaggle.com/datasets/gpreda/reddit-cryptocurrency)
2. [Kaggle reddit cryptocurrency related posts from various subreddits](https://www.kaggle.com/datasets/leukipp/reddit-crypto-data). Comments from subreddits: `'cryptocurrency', 'bitcoin', 'ethereum', 'dogecoin'` were extracted.

Final test data source is from https://www.surgehq.ai/datasets/crypto-sentiment-dataset.

### 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: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2823        | 1.0   | 5109  | 0.2658          | 0.8840   |
| 0.1905        | 2.0   | 10218 | 0.3070          | 0.8915   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-sentiment-reddit-crypto", "results": []}]}
transformers pytorch distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-sentiment-reddit-crypto
===============================================


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


* Loss: 0.3070
* Accuracy: 0.8915


Accuracy on the final test set was: 0.8641


Training and evaluation data
----------------------------


Training and validation data collected from 2 sources:


1. Kaggle reddit cryptocurrency posts and comments
2. Kaggle reddit cryptocurrency related posts from various subreddits. Comments from subreddits: ''cryptocurrency', 'bitcoin', 'ethereum', 'dogecoin'' were extracted.


Final test data source is from URL


### 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: 2


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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: 2 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 2 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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: 2### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2