---
language: multilingual
license: mit
library_name: torch
tags: []
base_model: BAAI/bge-m3
datasets: philipp-zettl/GGU-xx
metrics:
- accuracy
- f1
- recall
model_name: GGU-CLF
pipeline_tag: text-classification
widget:
- name: test1
  text: hello world
---

# Model Card for GGU-CLF

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



## Model Details

### Model Description

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


This is a simple classification model trained on a custom dataset.

Please note that this model, although it is implemented in the `transformers` library. Is not a usual transformer.
It combines the underlying embedding model with the required tokenizer into a simple-to-use pipeline for sequence classification.

It is used to classify user text into the following classes:
- 0: Greeting
- 1: Gratitude
- 2: Unknown

**Note**: To use this model please remember the following things

1. The model is an XLMRoberta model based on [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3).
2. The required tokenizer is baked into the classifier implementation.




- **Developed by:** [philipp-zettl](https://huggingface.co/philipp-zettl/)
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** multilingual
- **License:** mit
- **Finetuned from model [optional]:** BAAI/bge-m3

### Model Sources [optional]

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

- **Repository:** [philipp-zettl/GGU-CLF](https://huggingface.co/philipp-zettl/GGU-CLF)
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

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

Use this model to classify messages from natural language chats.

### 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. -->


The model was not trained on multi-sentence samples. **You should avoid those.**

Oficially tested and supported languages are **english and german** any other language is considered out of scope.


## 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.


```python
from transformers import AutoModel

model = AutoModel.from_pretrained("philipp-zettl/GGU-xx").to(torch.float16).to('cuda')

model([
    'Hi wie gehts?',
    'Dannke dir mein freund!',
    'Merci freundchen, send mir mal ein paar Machine Learning jobs.',
    'Works as expected, cheers!',
    'How you doin my boy',
    'send me immediately some matching jobs, thanks',
    'wer's eigentlich tom selleck?',
    'sprichst du deutsch?',
    'sprechen sie deutsch sie hurensohn?',
    'vergeltsgott',
    'heidenei dank dir recht herzlich',
    'grazie mille bambino, come estas'
]).argmax(dim=1)
```


## Training Details

### Training Data

<!-- This should link to a Dataset 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. -->


This model was trained using the [philipp-zettl/GGU-xx](https://huggingface.co/dataset/philipp-zettl/GGU-xx) dataset.

You can find it's performance metrics under [Evaluation Results](#evaluation-results).


### Training Procedure

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

#### Preprocessing [optional]


The following code was used to create the data set as well as split the data set into training and validation sets.

```python
from datasets import load_dataset

class Dataset:
    def __init__(self, dataset, target_names=None):
        self.data = list(map(lambda x: x[0], dataset))
        self.target = list(map(lambda x: x[1], dataset))
        self.target_names = target_names


ds = load_dataset('philipp-zettl/GGU-xx')
data = Dataset([[e['sample'], e['label']] for e in ds['train']], ['greeting', 'gratitude', 'unknown'])
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
```



#### 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 Dataset 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 -->


You can find the initial implementation of the classification model here:

```python
from transformers import PreTrainedModel, PretrainedConfig, AutoModel, AutoTokenizer
import torch
import torch.nn as nn


class EmbeddingClassifierConfig(PretrainedConfig):
    model_type = 'xlm-roberta'

    def __init__(self, num_classes=3, base_model='BAAI/bge-m3', tokenizer='BAAI/bge-m3', dropout=0.0, l2_reg=0.01, torch_dtype=torch.float16, **kwargs):
        self.num_classes = num_classes
        self.base_model = base_model
        self.tokenizer = tokenizer
        self.dropout = dropout
        self.l2_reg = l2_reg
        self.torch_dtype = torch_dtype
        super().__init__(**kwargs)


class EmbeddingClassifier(PreTrainedModel):
    config_class = EmbeddingClassifierConfig

    def __init__(self, config):
        super().__init__(config)
        base_model = config.base_model
        tokenizer = config.tokenizer

        if base_model is None or isinstance(tokenizer, str):
            base_model = AutoModel.from_pretrained(base_model)#, torch_dtype=config.torch_dtype)
        if tokenizer is None or isinstance(tokenizer, str):
            tokenizer = AutoTokenizer.from_pretrained(tokenizer)

        self.tokenizer = tokenizer
        self.base = base_model
        self.fc = nn.Linear(base_model.config.hidden_size, config.num_classes)#, torch_dtype=config.torch_dtype)
        self.do = nn.Dropout(config.dropout)#, torch_dtype=config.torch_dtype)
        self.l2_reg = config.l2_reg

        self.to(config.torch_dtype)

    def forward(self, X):
        encoding = self.tokenizer(
            X, return_tensors='pt',
            padding=True, truncation=True
        ).to(self.device)
        input_ids = encoding['input_ids']
        attention_mask = encoding['attention_mask']
        emb = self.base(
            input_ids,
            attention_mask=attention_mask,
            return_dict=True,
            output_hidden_states=True
        ).last_hidden_state[:, 0, :]
        return self.fc(self.do(emb))

    def train(self, set_val=True):
        self.base.train(False)
        for param in self.base.parameters():
            param.requires_grad = False
        for param in self.fc.parameters():
            param.requires_grad = set_val

    def get_l2_loss(self):
        l2_loss = torch.tensor(0.).to('cuda')
        for param in self.parameters():
            if param.requires_grad:
                l2_loss += torch.norm(param, 2)
        return self.l2_reg * l2_loss
```


## 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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### 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. -->

**BibTeX:**

[More Information Needed]

**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]

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## Model Card Authors [optional]

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## Model Card Contact

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