Initial upload

README.md

@@ -1,3 +1,300 @@
----
-license: apache-2.0
----
+---
+language:
+- de
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:8066634
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Diese drei geheimnisvollen Männer kamen uns dann zu Hilfe.
+  sentences:
+  - Drei ziemlich seltsame Typen halfen uns danach.
+  - Diese drei schwarzen Vögel sahen dann in unseren Garten.
+  - Einige Leute sind hilfsbereit.
+  - Un, zwei, drei... Wer kann die nächsten Zahlen erraten?
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# FMMB-BE-DE: The Fairly Multilingual ModernBERT Embedding Model (Belgian Edition): Monolingual German version.
+
+🇳🇱 This monolingual German version of the [Fairly Multilingual ModernBERT Embedding Model (Belgian Edition)](https://huggingface.co/Parallia/Fairly-Multilingual-ModernBERT-Embed-BE) is the perfect model for embedding texts up to 8192 tokens written in German at the speed of light. It uses the exact same weights as the original FMMB-BE model, and therefore produces identical embeddings, but this version comes with only a German-optimized tokenizer and its associated embedding table, thereby improving performance.
+
+🆘 This [sentence-transformers](https://www.SBERT.net) model was trained on a small parallel corpus containing English-French, English-Dutch, and English-German sentence pairs. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. The input texts can be used as-is, no need to use prefixes.
+
+🪄 Thanks to the magic of [Trans-Tokenization](https://huggingface.co/papers/2408.04303), monoligual English models such as [ModernBERT-Embed from Nomic AI](https://huggingface.co/nomic-ai/modernbert-embed-base) can be turned into embedding models for another language. And this, with almost no GPU compute involved! 🤯
+
+⚖️ Each of the 5 FMMB-BE models are actually copies of the exact same model, paired with different tokenizers and embedding tables. Indeed, as all trans-tokenized models operate on embeddings in the same latent space, aligning them cross-lingually is a breeze: after creating a "super" model which can speak in all of the 4 tokenizers, this model can be finetuned to produce similar embeddings for sentences which are translation of each other.
+
+⚡ ModernBERT, developped last month by Answer Ai and LightOn, is about 3x to 6x faster at inference time than regular BERT/RoBERTa models, while providing us with superior results. This makes it a wonderful choice for many use cases.
+
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [ModernBERT-Embed-Base](https://huggingface.co/nomic-ai/modernbert-embed-base)
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - parallel-sentences
+- **Languages:** nl
+- **License:** apache-2.0
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+**IMPORTANT:** While waiting for the next stable release of the `transformers` library, please install the latest git release to use `modernbert` models:
+
+```bash
+pip install --upgrade git+https://github.com/huggingface/transformers.git
+```
+
+The easiest way to use this model is to install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-DE")
+# Run inference
+sentences = [
+    'Diese drei geheimnisvollen Männer kamen uns dann zu Hilfe.',
+    'Drei ziemlich seltsame Typen halfen uns danach.',
+    'Diese drei schwarzen Vögel sahen dann in unseren Garten.',
+    'Einige Leute sind hilfsbereit.',
+    'Un, zwei, drei... Wer kann die nächsten Zahlen erraten?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [5, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [5, 5]
+```
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### parallel-sentences
+
+* Dataset: parallel dataset
+* Size: 8,066,634 training samples
+* Columns: <code>sent1</code> and <code>sent2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sent1                                                                             | sent2                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 17.86 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.87 tokens</li><li>max: 52 tokens</li></ul> |
+* Samples:
+  | sent1                                                                                                                                                     | sent2                                                                                                                                                                               |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>The faces may change, but the essential views that have characterised Israel’s government for decades will remain the same after 9 April</code> | <code>Les visages peuvent changer, mais les opinions fondamentales qui caractérisent le gouvernement israélien depuis des décennies resteront les mêmes après le 9 avril</code> |
+  | <code>- Yeah. My husband never talked about business.</code>                                                                                          | <code>M'n man had het nooit over z'n zaken.</code>                                                                                                                              |
+  | <code>Or do they think that We hear not their secrets and their private counsels?</code>                                                              | <code>Oder meinen sie, daß Wir ihre Geheimnisse und heimlichen Beratungen nicht hören?</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Framework Versions
+- Python: 3.11.7
+- Sentence Transformers: 3.3.1
+- Transformers: 4.48.0.dev0
+- PyTorch: 2.2.0+cu121
+- Accelerate: 1.0.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+If you use or finetune this model, please consider citing this paper and the sentence-transformers library:
+
+### BibTeX
+
+### This model
+```bibtex
+@misc{henderson2017efficient,
+    title={The Fairly Multilingual ModernBERT Embbeding Model -- Belgian Edition},
+    author={Francois Remy},
+    year={2025},
+    eprint={2501.99999},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```

config.json

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+{
+  "_name_or_path": "Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-DE",
+  "additional_special_tokens_ids": [],
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": null,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 2,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "mask_token_id": null,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": null,
+  "position_embedding_type": "absolute",
+  "reference_compile": false,
+  "sep_token_id": null,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0.dev0",
+  "unk_token_id": 0,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.0.dev0",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 594939640

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "special": true
+    }
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+  "legacy": true,
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+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}