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1_Pooling/config.json

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+{
+    "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

@@ -1,3 +1,458 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:527098
+- loss:MultipleNegativesRankingLoss
+base_model: NbAiLab/nb-bert-base
+widget:
+- source_sentence: The man talked to a girl over the internet camera.
+  sentences:
+  - A group of elderly people pose around a dining table.
+  - A teenager talks to a girl over a webcam.
+  - There is no 'still' that is not relative to some other object.
+- source_sentence: A woman is writing something.
+  sentences:
+  - Two eagles are perched on a branch.
+  - It refers to the maximum f-stop (which is defined as the ratio of focal length
+    to effective aperture diameter).
+  - A woman is chopping green onions.
+- source_sentence: The player shoots the winning points.
+  sentences:
+  - Minimum wage laws hurt the least skilled, least productive the most.
+  - The basketball player is about to score points for his team.
+  - Sheep are grazing in the field in front of a line of trees.
+- source_sentence: Stars form in star-formation regions, which itself develop from
+    molecular clouds.
+  sentences:
+  - Although I believe Searle is mistaken, I don't think you have found the problem.
+  - It may be possible for a solar system like ours to exist outside of a galaxy.
+  - A blond-haired child performing on the trumpet in front of a house while his younger
+    brother watches.
+- source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
+    consort, the King has always been the sovereign.
+  sentences:
+  - At first, I thought this is a bit of a tricky question.
+  - A man sitting on the floor in a room is strumming a guitar.
+  - There is a very good reason not to refer to the Queen's spouse as "King" - because
+    they aren't the King.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on NbAiLab/nb-bert-base
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev
+      type: sts-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.8478162865349333
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8495062962177747
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on NbAiLab/nb-bert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-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-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) <!-- at revision 9417c3f62a3adc99f17ff92bff446f35d011f994 -->
+- **Maximum Sequence Length:** 512 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/huggingface/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': 512, 'do_lower_case': False, 'architecture': '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
+    'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
+    'A man sitting on the floor in a room is strumming a guitar.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000,  0.5028, -0.1004],
+#         [ 0.5028,  1.0000, -0.0914],
+#         [-0.1004, -0.0914,  1.0000]])
+```
+
+<!--
+### 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: `sts-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8478     |
+| **spearman_cosine** | **0.8495** |
+
+<!--
+## 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: 527,098 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 20.91 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.91 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.14 tokens</li><li>max: 39 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                      | positive                                                                                                                                                                                                                    | negative                                                                         |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | <code>Det som følger er mindre en glid nedover en glatt skråning enn et profesjonelt skred som resulterer i enten en oppsigelse eller en smal flukt til neste drømmejobb, der, selvfølgelig, syklusen gjentas igjen.</code> | <code>Syklusen gjentar seg ved neste jobb.</code>                                                                                                                                                                           | <code>Syklusen gjentar seg sjelden ved neste jobb.</code>                        |
+  | <code>Syklusen gjentar seg ved neste jobb.</code>                                                                                                                                                                           | <code>Det som følger er mindre en glid nedover en glatt skråning enn et profesjonelt skred som resulterer i enten en oppsigelse eller en smal flukt til neste drømmejobb, der, selvfølgelig, syklusen gjentas igjen.</code> | <code>Syklusen gjentar seg sjelden ved neste jobb.</code>                        |
+  | <code>The public areas are spectacular, the rooms a bit less so, but a long-awaited renovation was carried out in 1998.</code>                                                                                              | <code>The rooms are nice, but the public area is in a league of it's own.</code>                                                                                                                                            | <code>The public area was fine, but the rooms were really something else.</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,500 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | float                                                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 16.18 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.15 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                         | sentence2                                             | score             |
+  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
+  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
+  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
+  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 128
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `warmup_steps`: 412.0
+- `bf16`: True
+- `eval_strategy`: steps
+- `per_device_eval_batch_size`: 128
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 128
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 412.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 1
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: True
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `eval_strategy`: steps
+- `per_device_eval_batch_size`: 128
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: False
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------:|
+| 0.0243 | 100  | 1.8923        | -               | -                       |
+| 0.0486 | 200  | 0.8525        | -               | -                       |
+| 0.0729 | 300  | 0.6760        | -               | -                       |
+| 0.0971 | 400  | 0.5891        | -               | -                       |
+| 0.1000 | 412  | -             | 1.5082          | 0.8397                  |
+| 0.1214 | 500  | 0.5567        | -               | -                       |
+| 0.1457 | 600  | 0.5245        | -               | -                       |
+| 0.1700 | 700  | 0.5024        | -               | -                       |
+| 0.1943 | 800  | 0.4654        | -               | -                       |
+| 0.2001 | 824  | -             | 1.5451          | 0.8390                  |
+| 0.2186 | 900  | 0.4796        | -               | -                       |
+| 0.2428 | 1000 | 0.4528        | -               | -                       |
+| 0.2671 | 1100 | 0.4577        | -               | -                       |
+| 0.2914 | 1200 | 0.4443        | -               | -                       |
+| 0.3001 | 1236 | -             | 1.5638          | 0.8455                  |
+| 0.3157 | 1300 | 0.4201        | -               | -                       |
+| 0.3400 | 1400 | 0.4010        | -               | -                       |
+| 0.3643 | 1500 | 0.4063        | -               | -                       |
+| 0.3885 | 1600 | 0.3955        | -               | -                       |
+| 0.4002 | 1648 | -             | 1.5716          | 0.8446                  |
+| 0.4128 | 1700 | 0.3798        | -               | -                       |
+| 0.4371 | 1800 | 0.3772        | -               | -                       |
+| 0.4614 | 1900 | 0.3933        | -               | -                       |
+| 0.4857 | 2000 | 0.3793        | -               | -                       |
+| 0.5002 | 2060 | -             | 1.5650          | 0.8499                  |
+| 0.5100 | 2100 | 0.3862        | -               | -                       |
+| 0.5342 | 2200 | 0.3730        | -               | -                       |
+| 0.5585 | 2300 | 0.3463        | -               | -                       |
+| 0.5828 | 2400 | 0.3556        | -               | -                       |
+| 0.6003 | 2472 | -             | 1.5952          | 0.8503                  |
+| 0.6071 | 2500 | 0.3614        | -               | -                       |
+| 0.6314 | 2600 | 0.3479        | -               | -                       |
+| 0.6557 | 2700 | 0.3508        | -               | -                       |
+| 0.6799 | 2800 | 0.3463        | -               | -                       |
+| 0.7003 | 2884 | -             | 1.5957          | 0.8471                  |
+| 0.7042 | 2900 | 0.3453        | -               | -                       |
+| 0.7285 | 3000 | 0.3327        | -               | -                       |
+| 0.7528 | 3100 | 0.3269        | -               | -                       |
+| 0.7771 | 3200 | 0.3333        | -               | -                       |
+| 0.8004 | 3296 | -             | 1.5891          | 0.8493                  |
+| 0.8014 | 3300 | 0.3370        | -               | -                       |
+| 0.8256 | 3400 | 0.3254        | -               | -                       |
+| 0.8499 | 3500 | 0.3348        | -               | -                       |
+| 0.8742 | 3600 | 0.3213        | -               | -                       |
+| 0.8985 | 3700 | 0.3376        | -               | -                       |
+| 0.9004 | 3708 | -             | 1.5881          | 0.8495                  |
+| 0.9228 | 3800 | 0.3362        | -               | -                       |
+| 0.9471 | 3900 | 0.3246        | -               | -                       |
+| 0.9713 | 4000 | 0.3215        | -               | -                       |
+| 0.9956 | 4100 | 0.3143        | -               | -                       |
+
+
+### Framework Versions
+- Python: 3.14.3
+- Sentence Transformers: 5.2.3
+- Transformers: 5.3.0
+- PyTorch: 2.10.0+cu130
+- Accelerate: 1.13.0
+- Datasets: 4.6.1
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### 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}
+}
+```
+
+<!--
+## 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 +1,36 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "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",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "5.3.0",
+    "pytorch": "2.10.0+cu130"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cc4842e3a7d0a2c0bff737ce78bb5abb7cbfea3fdbf97dd8955ed080627bb1c
+size 711436112

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": 512,
+    "do_lower_case": false
+}

tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}