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---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
<!-- Provide a quick summary of what the model is/does. -->
The [SwissBERT](https://huggingface.co/ZurichNLP/swissbert) model 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/6564ab8d113e2baa55830af0/1Ac61s_mlgW5aL6OnS6ay.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
def generate_sentence_embedding(sentence, model_name="jgrosjean-mathesis/swissbert-for-sentence-embeddings"):
# Load swissBERT model
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model.set_default_language("de_CH")
# Tokenize input sentence
inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt", max_length=512)
# Set the model to evaluation mode
model.eval()
# 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.
This multilingual model has not been fine-tuned for cross-lingual transfer. It is intended for computing sentence embeddings that can be compared mono-lingually.
## 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:**
[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]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
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