Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:83246
+- loss:CosineSimilarityLoss
+base_model: CALDISS-AAU/DA-BERT_Old_News_V3
+widget:
+- source_sentence: og jeg derefter er underrettet, at han i Mandags Morges Kl. 5,
+    skal have paa en tyvagtig Maade krøbet over mit Plankeværk ind i min Gaard gaaet
+    ind i sit Kammer, taget endeel Sager, og derefter er han igien gaaet bort, uden
+    at nogen veed hvor han er bleven af, saa nødes jeg herved at efterlyse bemeldte
+    Niels Stephensen, med Anmodning at han vil indfinde sig, for at giøre Rigtighed
+    for anseelige Penge og videre som ham er betroet
+  sentences:
+  - saa bliver han end videre herved efterlyst, med Erindring til alle og enhver,
+    ikke enten at huse, hæle eller i Tieneste antage denne Tyv, men de hannem maatte
+    forekomme, anmode hannem at anholde og mig derom notificere
+  - ombedes ingen at huse eller hæle med ham, ei heller at antage ham i nogen Slags
+    Tieneste
+  - saa ombedes alle og enhver, om bemeldte Pige skulde forlange noget i mit Navn,
+    hun da ei maa betroes
+- source_sentence: da han ved alle Leyligheder, og Steder her i Landet er eftersøgt,
+    men ey bleven funden eller nogen Oplysning om ham faaet
+  sentences:
+  - og hans Broder Hans Witt, der skal opholde sig i Sielland,
+  - er Natten imellem den 6te og 2de et Mai sidstleden undvigt Reserven Jørnen Andersen
+    Dannemarre.
+  - En skikkelig Karl søger Gaardskarls Plads;
+- source_sentence: uden at hans Opholdsted har været at opdage
+  sentences:
+  - er efter at have begaaet stor Utroskab bortløben den 12 hujus af min Tieneste
+  - som haver paa en utilbørlig Maade forladt Staden og Mesterens Arbeide, og er paa
+    Herberget og ved Laden skyldig
+  - igen at indfinde sig paa Districter, og søge den Tieneste, hvortil han har fæstet
+    sig, hos Bonden Oluf Nielsen i Pederstrup Sogn i bemeldte Ballerup
+- source_sentence: En tyk undersætsig Pige ved Navn Kirsten, som har tient mig fra
+    Michelsdag af, er bortgaaet af sin Tieneste
+  sentences:
+  - 'saaledes undvigt begge uden allerringeste beføyet Aarsag nemlig: den Ene navnlig
+    Ole Espensen af Anders Andersens Læg i Høng, 23 a 24 Aar gammel [...] og den Anden
+    navnlig Niels Hansen [...] aldeles uden mindste Føye af sin Tieneste undvigt og
+    vides ikke hvor er'
+  - den Mulct den sig paadrager, i Følge Forordningen af 4de Februar. 1733. den 19de
+    Post som slig Bortrømt huser eller hæle
+  - indkaldes herved sub pæna præclusi & perpetui silentii, med Aar og Dags Varsel
+    alle Dem som formeene sig at have noget at fordre hos S. T. Hr. Assessor Mathias
+    Leth Sommerhielm, eller i hens Boe efter hans ved Døden afgangne Hustrue, Frue
+    Friderikke Dorothea Sehestedt paa Tomb Gaard i Smaalenenes Amt, af hvad Beskaffenhed
+    og under hvad Titul deres Fordringer end maatte være der ved at fremkomme og saadant
+    deres havende Krav inden forbemeldte Tids Forløb for ham at anmelde og beviisliggiøre.
+- source_sentence: saa finde vi os beføyet, saavel for at besørge de publiqve Midlers
+    Stkkerhed, som i Anledning af den Nød og Armod hans Undvigelse har foraarsaget
+    hans efterladte Kone og smaa Børn her, at lade ham herved eftelyse og indkalde
+    til at indfinde sig her Byen
+  sentences:
+  - men ingen veed om ham at sige.
+  - Friseur W. har logeret hos mig i lille Kongensgade og absenteret sig i April Maaned,
+    samt efterladt sig en luk Kuffert, saa bedes han at komme til mig inden 8 Dage
+    for at gjøre Rigtighed, hvis ikke, sættes hans efterladte Tøi paa Auction og forbeholdes
+    min Ret.
+  - thi anmodes enhver som træffer denne Karl at paagribe ham og imod de sædvanlige
+    Indbringerpenge at levere ham til Compagniet.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on CALDISS-AAU/DA-BERT_Old_News_V3
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: validation
+      type: validation
+    metrics:
+    - type: pearson_cosine
+      value: 0.8931725385239138
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8380469537534867
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on CALDISS-AAU/DA-BERT_Old_News_V3
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [CALDISS-AAU/DA-BERT_Old_News_V3](https://huggingface.co/CALDISS-AAU/DA-BERT_Old_News_V3). 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [CALDISS-AAU/DA-BERT_Old_News_V3](https://huggingface.co/CALDISS-AAU/DA-BERT_Old_News_V3) <!-- at revision 4ee1d0adc4713b6ca089da767a2e5bbf07b3a9aa -->
+- **Maximum Sequence Length:** 514 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (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
+
+### Direct Usage (Sentence Transformers)
+
+First 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("JohanHeinsen/Run_Away_Action_Embedding_model_v0.1")
+# Run inference
+sentences = [
+    'saa finde vi os beføyet, saavel for at besørge de publiqve Midlers Stkkerhed, som i Anledning af den Nød og Armod hans Undvigelse har foraarsaget hans efterladte Kone og smaa Børn her, at lade ham herved eftelyse og indkalde til at indfinde sig her Byen',
+    'thi anmodes enhver som træffer denne Karl at paagribe ham og imod de sædvanlige Indbringerpenge at levere ham til Compagniet.',
+    'men ingen veed om ham at sige.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `validation`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| pearson_cosine      | 0.8932    |
+| **spearman_cosine** | **0.838** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 83,246 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                        | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | int                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 28.53 tokens</li><li>max: 176 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 30.7 tokens</li><li>max: 514 tokens</li></ul> | <ul><li>0: ~47.20%</li><li>1: ~52.80%</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                   | sentence_1                                                                                                                                                             | label          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Han er født i Kjøbenhavn, er 33 Aar gammel, har staaet ved Kongens Regiment som Tambour i 9 Aar, og gaaer derfor skjæbt paa det venstre Bern har tjent Hr. Fabriker Schrøder, og paa Maderup gaard som Gaardskarl, en kort Tid.</code> | <code>som tiener paa Hiørnet af gl. Mynt og Svertegaden</code>                                                                                                         | <code>1</code> |
+  | <code>efterlyses han herved med Anmodning, at han, af hvem han skulde forekomme, maatte anholdes og mig derom meddeles Underretning</code>                                                                                                   | <code>Da min Lærredreng Friderich Senggrav er den 12 Aug. undvigt af sin Lære</code>                                                                                   | <code>0</code> |
+  | <code>give det tilkiende hos A. J. Schottlænder, boende i Pilestræde Nr. 11 Litr. Dførste Sal.</code>                                                                                                                                        | <code>Niels Pedersen Eistrup, som i 4 Aar har tjent hos mig for Under-Knegt, er fra mig undvigt den 24 December uden at giøre Rigtighed for det ham var betroet</code> | <code>0</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 2
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: False
+- `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}
+- `tp_size`: 0
+- `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
+- `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`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | validation_spearman_cosine |
+|:------:|:-----:|:-------------:|:--------------------------:|
+| 0.0961 | 500   | 0.1933        | -                          |
+| 0.1922 | 1000  | 0.1191        | -                          |
+| 0.2883 | 1500  | 0.1016        | -                          |
+| 0.3844 | 2000  | 0.0914        | -                          |
+| 0.4805 | 2500  | 0.0841        | -                          |
+| 0.5766 | 3000  | 0.079         | -                          |
+| 0.6727 | 3500  | 0.0757        | -                          |
+| 0.7688 | 4000  | 0.0732        | -                          |
+| 0.8649 | 4500  | 0.0686        | -                          |
+| 0.9610 | 5000  | 0.0665        | -                          |
+| 1.0    | 5203  | -             | 0.8284                     |
+| 1.0571 | 5500  | 0.0627        | -                          |
+| 1.1532 | 6000  | 0.0548        | -                          |
+| 1.2493 | 6500  | 0.0557        | -                          |
+| 1.3454 | 7000  | 0.0562        | -                          |
+| 1.4415 | 7500  | 0.0557        | -                          |
+| 1.5376 | 8000  | 0.0527        | -                          |
+| 1.6337 | 8500  | 0.0504        | -                          |
+| 1.7298 | 9000  | 0.0539        | -                          |
+| 1.8259 | 9500  | 0.054         | -                          |
+| 1.9220 | 10000 | 0.0501        | -                          |
+| 2.0    | 10406 | -             | 0.8380                     |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.7.0
+- Accelerate: 1.6.0
+- Datasets: 2.19.2
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50005
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.7.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:520c3ec1179dc00e10e5639e640ac1e4ffb1e97ad3f0a4f0d9c395f1c022a91e
+size 497806184

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 514,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
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+  "pad_token": "[PAD]",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}