|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: distilBERT_ESG |
|
results: [] |
|
datasets: |
|
- TrajanovRisto/esg-sentiment |
|
language: |
|
- en |
|
widget: |
|
- text: "Our waste reduction initiatives aim to minimize environmental impact." |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# distilBERT_ESG |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2591 |
|
- F1: 0.6296 |
|
- Roc Auc: 0.7569 |
|
- Accuracy: 0.3824 |
|
|
|
## Model description |
|
|
|
This repository contains a fine-tuned DistilBERT model using the [esg-sentiment dataset](https://huggingface.co/datasets/TrajanovRisto/esg-sentiment). DistilBERT, a distilled version of BERT, is a powerful transformer-based model for natural language processing tasks. The model has been fine-tuned on the ESG (Environmental, Social, and Governance) sentiment dataset, allowing it to capture nuanced sentiments related to sustainability and corporate responsibility. |
|
|
|
### Features |
|
|
|
- DistilBERT-based architecture |
|
- Fine-tuned on the esg-sentiment dataset |
|
- Optimized for sentiment analysis in the context of ESG |
|
|
|
## Intended uses & limitations |
|
|
|
### Intended Uses |
|
|
|
The fine-tuned DistilBERT model is designed for sentiment analysis tasks related to ESG considerations. It can be used to analyze and classify text data, providing insights into the sentiment towards environmental, social, and governance practices. |
|
|
|
### Limitations |
|
|
|
- The model's performance is directly influenced by the quality and diversity of the training data. |
|
- It may not generalize well to domains outside the ESG context. |
|
- Users are encouraged to validate results on their specific use cases and datasets. |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
|
| No log | 1.0 | 77 | 0.3731 | 0.4 | 0.6322 | 0.2647 | |
|
| No log | 2.0 | 154 | 0.3158 | 0.2342 | 0.5651 | 0.1324 | |
|
| No log | 3.0 | 231 | 0.2773 | 0.5 | 0.6791 | 0.3382 | |
|
| No log | 4.0 | 308 | 0.2636 | 0.6049 | 0.7442 | 0.3382 | |
|
| No log | 5.0 | 385 | 0.2591 | 0.6296 | 0.7569 | 0.3824 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
