Feature Extraction
Transformers
Safetensors
modernbert
html
document-embedding
text-embeddings-inference
Instructions to use Seznam/html-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seznam/html-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Seznam/html-lm")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Seznam/html-lm") model = AutoModel.from_pretrained("Seznam/html-lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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The model was trained from scratch for one pass over the training corpus using a combination of:
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* Masked Language Modeling (MLM)
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* Bag-of-Words prediction from `[CLS]`
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* Contrastive distillation from [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) and [Seznam SeLLMa 8B](https://blog.seznam.cz/2024/10/diana-hlavacova-sellma-aneb-jak-v-seznamu-krotime-drave-jazykove-modely/)
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## Training data
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The model was trained from scratch for one pass over the training corpus using a combination of:
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* Masked Language Modeling (MLM)
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* [Bag-of-Words prediction](https://arxiv.org/abs/2401.11248) from `[CLS]`
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* Contrastive distillation from [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) and [Seznam SeLLMa 8B](https://blog.seznam.cz/2024/10/diana-hlavacova-sellma-aneb-jak-v-seznamu-krotime-drave-jazykove-modely/)
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## Training data
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