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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+---
+base_model: NbAiLab/nb-sbert-base
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- dot_accuracy
+- manhattan_accuracy
+- euclidean_accuracy
+- max_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:96724
+- loss:TripletLoss
+- loss:MultipleNegativesRankingLoss
+- loss:CoSENTLoss
+widget:
+- source_sentence: Fjerne 60 cm snø fra enebolig på 100 kvadratmeter
+  sentences:
+  - 'query: montere solskjerming inne'
+  - 'query: 150 meter grøfting'
+  - 'query: Snømåking på enebolig, 100 kvadratmeter'
+- source_sentence: Renovering av bad
+  sentences:
+  - Asfaltere innkjørsel
+  - Nye garasjeporter m/åpner
+  - Totalrenovering av lite bad i Lillestrøm
+- source_sentence: Lite tilbygg til eksisterende bolig
+  sentences:
+  - Renovere bolig
+  - Vi skal pusse opp kjøkken
+  - Bygge tilbygg
+- source_sentence: Gulvlegging 6 kvm gang
+  sentences:
+  - Installere gulvvarme
+  - Montering av 8  spotlights brannsikre  (4stk. på kjøket) og (2 stk i gangen)
+  - Legge parkett i gang
+- source_sentence: Fullføre utvendig forefallent arbeid
+  sentences:
+  - Bytte av vinduer i hus
+  - elektriker på bolig på 120kvm
+  - Renovere bad
+model-index:
+- name: SentenceTransformer based on NbAiLab/nb-sbert-base
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: test triplet evaluation
+      type: test-triplet-evaluation
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9859055673009162
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.016913319238900635
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.9844961240310077
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.9837914023960536
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.9859055673009162
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on NbAiLab/nb-sbert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base). 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:** [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base) <!-- at revision 56ae460305b0787432b6498e5adc17447e66fe66 -->
+- **Maximum Sequence Length:** 75 tokens
+- **Output Dimensionality:** 768 tokens
+- **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': 75, 'do_lower_case': False}) with Transformer model: BertModel 
+  (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("ostoveland/SBertBaseMittanbudver1")
+# Run inference
+sentences = [
+    'Fullføre utvendig forefallent arbeid',
+    'elektriker på bolig på 120kvm',
+    'Renovere bad',
+]
+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
+
+#### Triplet
+* Dataset: `test-triplet-evaluation`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.9859     |
+| dot_accuracy       | 0.0169     |
+| manhattan_accuracy | 0.9845     |
+| euclidean_accuracy | 0.9838     |
+| **max_accuracy**   | **0.9859** |
+
+<!--
+## 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 Datasets
+
+#### Unnamed Dataset
+
+
+* Size: 55,426 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | sentence_2                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.92 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.49 tokens</li><li>max: 35 tokens</li></ul> |
+* Samples:
+  | sentence_0                              | sentence_1                                | sentence_2                                                       |
+  |:----------------------------------------|:------------------------------------------|:-----------------------------------------------------------------|
+  | <code>Bygge støttemur</code>            | <code>Støttemur</code>                    | <code>Bytte lås på dörr</code>                                   |
+  | <code>Understell bord i stål</code>     | <code>Lage stålunderstell til bord</code> | <code>Bygge trebord</code>                                       |
+  | <code>Reparasjon vannbåren varme</code> | <code>Vannbåren varme til enebolig</code> | <code>* Fortsatt ledig: ombygning av eksisterende kjeller</code> |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+#### Unnamed Dataset
+
+
+* Size: 22,563 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 11.09 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.94 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                 | sentence_1                              |
+  |:-------------------------------------------------------------------------------------------|:----------------------------------------|
+  | <code>utforing av gavlvegg</code>                                                          | <code>query: utforing av vegg</code>    |
+  | <code>Montere kjøkken</code>                                                               | <code>query: kjøkkenmontering</code>    |
+  | <code>Sette opp lettvegg med skyvedør, bygge bod i carport, forlenge tak på carport</code> | <code>query: bygge bod i carport</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"
+  }
+  ```
+
+#### Unnamed Dataset
+
+
+* Size: 18,735 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                                                                           | float                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 13.08 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.52 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.05</li><li>mean: 0.51</li><li>max: 0.95</li></ul> |
+* Samples:
+  | sentence_0                                               | sentence_1                                 | label             |
+  |:---------------------------------------------------------|:-------------------------------------------|:------------------|
+  | <code>Renovering av hus - plantegninger og fasade</code> | <code>elektriker på bolig på 120kvm</code> | <code>0.15</code> |
+  | <code>Blending av innvendig dør</code>                   | <code>Tette igjen døråpning</code>         | <code>0.75</code> |
+  | <code>Fortsatt ledig: Kappe teglstein på pipeløp</code>  | <code>Murearbeid</code>                    | <code>0.45</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 6
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 6
+- `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}
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | test-triplet-evaluation_max_accuracy |
+|:------:|:-----:|:-------------:|:------------------------------------:|
+| 0.2844 | 500   | 3.6092        | -                                    |
+| 0.5688 | 1000  | 2.9852        | -                                    |
+| 0.8532 | 1500  | 2.7542        | -                                    |
+| 1.0011 | 1760  | -             | 0.9831                               |
+| 1.1365 | 2000  | 2.5467        | -                                    |
+| 1.4209 | 2500  | 2.3263        | -                                    |
+| 1.7053 | 3000  | 2.2608        | -                                    |
+| 1.9898 | 3500  | 2.2042        | -                                    |
+| 2.0011 | 3520  | -             | 0.9859                               |
+| 2.2730 | 4000  | 2.1615        | -                                    |
+| 2.5575 | 4500  | 2.0934        | -                                    |
+| 2.8419 | 5000  | 2.1226        | -                                    |
+| 3.0011 | 5280  | -             | 0.9859                               |
+| 3.1251 | 5500  | 2.1977        | -                                    |
+| 3.4096 | 6000  | 2.1209        | -                                    |
+| 3.6940 | 6500  | 2.1006        | -                                    |
+| 3.9784 | 7000  | 2.1495        | -                                    |
+| 4.0011 | 7040  | -             | 0.9859                               |
+| 4.2617 | 7500  | 2.1792        | -                                    |
+| 4.5461 | 8000  | 2.0958        | -                                    |
+| 4.8305 | 8500  | 2.1065        | -                                    |
+| 5.0011 | 8800  | -             | 0.9859                               |
+| 5.1138 | 9000  | 2.1762        | -                                    |
+| 5.3982 | 9500  | 2.1347        | -                                    |
+| 5.6826 | 10000 | 2.1198        | -                                    |
+| 5.9670 | 10500 | 2.1251        | -                                    |
+| 5.9943 | 10548 | -             | 0.9859                               |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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 +1,32 @@
+{
+  "_name_or_path": "NbAiLab/nb-sbert-base",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "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-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6dcb7ea1adbe8fe668ce4846d4f85ecba37ad4789ad0750397d737d019fb162
+size 711480934

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 75,
+  "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 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
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+}