README.md

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

The [SwissBERT](https://huggingface.co/ZurichNLP/swissbert) model was finetuned via [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (Gao et al., EMNLP 2021) for sentence embeddings, using ~1 million Swiss news articles published in 2022 from [Swissdox@LiRI](https://t.uzh.ch/1hI). Following the [Sentence Transformers](https://huggingface.co/sentence-transformers) approach (Reimers and Gurevych,
2019), the average of the last hidden states (pooler_type=avg) is used as sentence representation.

The fine-tuning script can be accessed [here](Link).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6564ab8d113e2baa55830af0/zUUu7WLJdkM2hrIE5ev8L.png)

## Model Details

### Model Description

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

- **Developed by:** [Juri Grosjean](https://huggingface.co/jgrosjean)
- **Model type:** [XMOD](https://huggingface.co/facebook/xmod-base)
- **Language(s) (NLP):** de_CH, fr_CH, it_CH, rm_CH
- **License:** [More Information Needed]
- **Finetuned from model:** [SwissBERT](https://huggingface.co/ZurichNLP/swissbert)

## Use

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

```python
import torch

from transformers import AutoModel, AutoTokenizer

### German example

# Load swissBERT for sentence embeddings model
model_name="jgrosjean-mathesis/swissbert-for-sentence-embeddings"
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model.set_default_language("de_CH")

def generate_sentence_embedding(sentence, ):

    # Tokenize input sentence
    inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt", max_length=512)

    # Take tokenized input and pass it through the model
    with torch.no_grad():
        outputs = model(**inputs)

    # Extract average sentence embeddings from the last hidden layer
    embedding = outputs.last_hidden_state.mean(dim=1)

    return embedding

sentence_embedding = generate_sentence_embedding("Wir feiern am 1. August den Schweizer Nationalfeiertag.")
print(sentence_embedding)
```
Output:
```
tensor([[ 5.6306e-02, -2.8375e-01, -4.1495e-02,  7.4393e-02, -3.1552e-01,
          1.5213e-01, -1.0258e-01,  2.2790e-01, -3.5968e-02,  3.1769e-01,
          1.9354e-01,  1.9748e-02, -1.5236e-01, -2.2657e-01,  1.3345e-02,
        ...]])
```

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model has been trained on news articles only. Hence, it might not perform as well on other text classes.

## 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. -->

[More Information Needed]

### 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]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** python3 train_simcse_multilingual.py \
  --seed 54699 \
  --model_name_or_path zurichNLP/swissbert \
  --train_file /srv/scratch2/grosjean/Masterarbeit/data_subsets \
  --output_dir /srv/scratch2/grosjean/Masterarbeit/model \
  --overwrite_output_dir \
  --save_strategy no \
  --do_train \
  --num_train_epochs 1 \
  --learning_rate 1e-5 \
  --per_device_train_batch_size 4 \
  --gradient_accumulation_steps 128 \
  --max_seq_length 512 \
  --overwrite_cache \
  --pooler_type avg \
  --pad_to_max_length \
  --temp 0.05 \
  --fp16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

[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



## 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:**

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**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]

[More Information Needed]

## Model Card Contact

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