---
library_name: transformers
language:
- en
pipeline_tag: text-classification
---

# Model Card for Model ID

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



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Manuel Fernández
- **Funded by [optional]:** N/A
- **Shared by [optional]:** Manuel Fernández
- **Model type:** Classification
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** bert-base-multilingual-cased

### Model Sources [optional]

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

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

This model was fined tuned beacuse it is part of course of huggingface about NLP 🤗🤗🤗.

### Direct Use

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

Text Classification


## Bias, Risks, and Limitations

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

Same Bias, Risks and Limitations the model bert-base-multilingual-cased

### 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.

```python
import transformers

tokenizer = transformers.AutoTokenizer.from_pretrained("manyah/bert-base-multilingual-cased-trainer")
model = transformers.AutoModel.from_pretrained("manyah/bert-base-multilingual-cased-trainer")

```