README.md

---
license: cc-by-2.0
language:
- bg
pipeline_tag: text-generation
---

# Model Card for GPT-WEB-BG

<!-- Provide a quick summary of what the model is/does. -->

This model is pre-trained with the causal language modelling objective on a private dataset with web scraped content created at the Bulgarian Academy of Sciences under the [ClaDa-BG Project](https://clada-bg.eu/en/).

The dataset is cleaned and balanced with a specialized procedure to avoid gender, cultural, political, racial and other biases. The procedure is described in the paper dedicated to this model- coming soon!

## Model Details


### Model Description

The model is the first from a series of Large Languege Models for Bulgarian. 



- **Developed by:** [Iva Marinova](https://huggingface.co/usmiva)
- **Shared by [optional]:** ClaDa-BG, : National Interdisciplinary Research E-Infrastructure for Bulgarian Language and Cultural Heritage Resources and Technologies integrated within European CLARIN and DARIAH infrastructures
- **Model type:** GPT-2
- **Language(s) (NLP):** Bulgarian
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** Marinova et. al. 2023 - link to be added
- **Demo [optional]:** [More Information Needed]

## Uses

The model is trained on the causal language modeling objective and can be used to generate content based on textual input. It can be further finetuned for specific NLP tasks in the online media domain such as Event Extraction, Relation Extracation, Named Entity Recognition, etc.
This model is intended for use from researchers and practitioners in the NLP field. 

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

Using pipeline

```python
from transformers import pipeline, set_seed
gpt_web_bg = pipeline('text-generation', model='/usmiva/gpt_web_bg', max_length=50, num_beams=3, temperature=0.8)
set_seed(42)
```

```python
gpt_web_bg("По професия той е ")

```
```
[{'generated_text': 'По професия той е строителен работник, който е �'}]
```



## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->


### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

```
{
  "activation_function": "gelu_new",
  "architectures": [
    "GPT2LMHeadModel"
  ],
  "attn_pdrop": 0.1,
  "bos_token_id": 50256,
  "embd_pdrop": 0.1,
  "eos_token_id": 50256,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "gpt2",
  "n_embd": 768,
  "n_head": 12,
  "n_inner": null,
  "n_layer": 12,
  "n_positions": 1024,
  "reorder_and_upcast_attn": false,
  "resid_pdrop": 0.1,
  "scale_attn_by_inverse_layer_idx": false,
  "scale_attn_weights": true,
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "torch_dtype": "float32",
  "transformers_version": "4.22.0.dev0",
  "use_cache": true,
  "vocab_size": 50257
}
```

#### Preprocessing [optional]

The process of creating a diverse, bias-proof, and ethically fair dataset requires a meticulous and effective approach to clean the raw text data extracted from the internet. To address this challenge, we propose a specialized, multi-step procedure organized into the following stages:

**Deduplication** - Duplicate text sequences, often caused by web scraping, are removed from the dataset, thus ensuring that each entry contributes unique information to the training data.
**Topic Classification** - To guarantee diverse subject matter and reduce the risk of topic bias, topic classification is employed to categorize text entries based on their content.
**Sentiment Classification** - By categorizing entries with sentiment, the dataset diversity is further enhanced, enabling models to better interpret and handle the inherent emotional aspects of human language.
**Hate-Speech Detection** - To exclude content promoting hate speech from the dataset, automatic detection methods for Bulgarian are utilized.
**Balancing Topics and Sentiment in the Data** - The emphasis is placed on ensuring an adequate balance between topics and sentiment classes, as an imbalanced dataset can lead to biased results. By carefully redistributing instances across topics and sentiment categories, a more representative and inclusive dataset can be assembled, resulting in more robust and adaptable models.
**Cleaning Abusive Content** - To further refine the dataset, abusive content, including profanities, vulgar language, and other offensive expressions were cleaned from the text utilizing algorithms for abusive language detection.
**Minimum Sentence Threshold** - To ensure that the dataset includes meaningful and coherent text instances, a minimum sentence threshold is imposed, requiring that each entry contains at least five sentences. This condition ensures that models are trained on richer linguistic contexts and promotes more accurate and nuanced text generation.
**Cleaning non Bulgarian content**


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

The model is trained BERT with the masked language modelling objective from scratch on Bulgarian dataset containing data from opensource online media.  

### Compute Infrastructure

1 NVIDIA V100 video card

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
Transformer-Based Language Models for Bulgarian, Marinova et. al. - TBA

**BibTeX:**

TBA

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]