Instructions to use deepset/gbert-large-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-large-sts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepset/gbert-large-sts")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepset/gbert-large-sts") model = AutoModelForSequenceClassification.from_pretrained("deepset/gbert-large-sts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: de | |
| license: mit | |
| tags: | |
| - exbert | |
| ## Overview | |
| **Language model:** gbert-large-sts | |
| **Language:** German | |
| **Training data:** German STS benchmark train and dev set | |
| **Eval data:** German STS benchmark test set | |
| **Infrastructure**: 1x V100 GPU | |
| **Published**: August 12th, 2021 | |
| ## Details | |
| - We trained a gbert-large model on the task of estimating semantic similarity of German-language text pairs. The dataset is a machine-translated version of the [STS benchmark](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark), which is available [here](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark). | |
| ## Hyperparameters | |
| ``` | |
| batch_size = 16 | |
| n_epochs = 4 | |
| warmup_ratio = 0.1 | |
| learning_rate = 2e-5 | |
| lr_schedule = LinearWarmup | |
| ``` | |
| ## Performance | |
| Stay tuned... and watch out for new papers on arxiv.org ;) | |
| ## Authors | |
| - Julian Risch: `julian.risch [at] deepset.ai` | |
| - Timo Möller: `timo.moeller [at] deepset.ai` | |
| - Julian Gutsch: `julian.gutsch [at] deepset.ai` | |
| - Malte Pietsch: `malte.pietsch [at] deepset.ai` | |
| ## About us | |
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| <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> | |
| <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> | |
| </div> | |
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| <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> | |
| </div> | |
| </div> | |
| [deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/). | |
| Some of our other work: | |
| - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2) | |
| - [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) | |
| - [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio) | |
| ## Get in touch and join the Haystack community | |
| <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. | |
| We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> | |
| [Twitter](https://twitter.com/Haystack_AI) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai) | |
| By the way: [we're hiring!](http://www.deepset.ai/jobs) |