Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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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+---
+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:9504
+- loss:TripletLoss
+widget:
+- source_sentence: cap product
+  sentences:
+  - method of adjoining a chain of degree p with a co-chain of degree q, where q is
+    less than or equal to p, to form a composite chain of degree p-q
+  - 'Ontology '
+  - hat commodity
+- source_sentence: cognitivism
+  sentences:
+  - supporting cognitive science
+  - study of changes in organisms caused by modification of gene expression rather
+    than alteration of the genetic code
+  - 'the idea that mind works like an algorithmic symbol manipulation '
+- source_sentence: doxastic voluntarism
+  sentences:
+  - Land surrounded by water
+  - belief one is free
+  - the ability to will beliefs
+- source_sentence: conceptual role
+  sentences:
+  - concept
+  - inferential role
+  - 'Theory of knowledge '
+- source_sentence: scientific revolutions
+  sentences:
+  - scientific realism
+  - Universal moral principles govern legal systems
+  - paradigm shifts
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: beatai dev
+      type: beatai-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8080808080808081
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.28114478114478114
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8316498316498316
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8249158249158249
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8316498316498316
+      name: Max Accuracy
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-20e")
+# Run inference
+sentences = [
+    'scientific revolutions',
+    'paradigm shifts',
+    'scientific realism',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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: `beatai-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric             | Value      |
+|:-------------------|:-----------|
+| cosine_accuracy    | 0.8081     |
+| dot_accuracy       | 0.2811     |
+| manhattan_accuracy | 0.8316     |
+| euclidean_accuracy | 0.8249     |
+| **max_accuracy**   | **0.8316** |
+
+<!--
+## 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 Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 138
+- `per_device_eval_batch_size`: 138
+- `learning_rate`: 2e-06
+- `num_train_epochs`: 10
+- `lr_scheduler_type`: constant
+- `bf16`: True
+- `dataloader_drop_last`: 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`: 138
+- `per_device_eval_batch_size`: 138
+- `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-06
+- `weight_decay`: 0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: constant
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 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`: 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`: True
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: 2
+- `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
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   | beatai-dev_max_accuracy |
+|:------:|:----:|:-------------:|:------:|:-----------------------:|
+| 0      | 0    | -             | -      | 0.8072                  |
+| 0.1471 | 10   | 1.8573        | -      | -                       |
+| 0.2941 | 20   | 1.8196        | -      | -                       |
+| 0.4412 | 30   | 1.8594        | -      | -                       |
+| 0.5882 | 40   | 1.8581        | -      | -                       |
+| 0.7353 | 50   | 1.8766        | 2.3603 | 0.8047                  |
+| 0.8824 | 60   | 1.8596        | -      | -                       |
+| 1.0294 | 70   | 1.6816        | -      | -                       |
+| 1.1765 | 80   | 1.7564        | -      | -                       |
+| 1.3235 | 90   | 1.7191        | -      | -                       |
+| 1.4706 | 100  | 1.6521        | 2.3296 | 0.8064                  |
+| 1.6176 | 110  | 1.7054        | -      | -                       |
+| 1.7647 | 120  | 1.6895        | -      | -                       |
+| 1.9118 | 130  | 1.6724        | -      | -                       |
+| 2.0588 | 140  | 1.6369        | -      | -                       |
+| 2.2059 | 150  | 1.705         | 2.2941 | 0.8123                  |
+| 2.3529 | 160  | 1.8329        | -      | -                       |
+| 2.5    | 170  | 1.6071        | -      | -                       |
+| 2.6471 | 180  | 1.5157        | -      | -                       |
+| 2.7941 | 190  | 1.624         | -      | -                       |
+| 2.9412 | 200  | 1.6185        | 2.2668 | 0.8140                  |
+| 3.0882 | 210  | 1.6259        | -      | -                       |
+| 3.2353 | 220  | 1.5749        | -      | -                       |
+| 3.3824 | 230  | 1.5426        | -      | -                       |
+| 3.5294 | 240  | 1.5522        | -      | -                       |
+| 3.6765 | 250  | 1.5141        | 2.2498 | 0.8157                  |
+| 3.8235 | 260  | 1.5215        | -      | -                       |
+| 3.9706 | 270  | 1.4983        | -      | -                       |
+| 4.1176 | 280  | 1.4819        | -      | -                       |
+| 4.2647 | 290  | 1.4552        | -      | -                       |
+| 4.4118 | 300  | 1.5597        | 2.2226 | 0.8199                  |
+| 4.5588 | 310  | 1.3983        | -      | -                       |
+| 4.7059 | 320  | 1.5386        | -      | -                       |
+| 4.8529 | 330  | 1.4541        | -      | -                       |
+| 5.0    | 340  | 1.4097        | -      | -                       |
+| 5.1471 | 350  | 1.3741        | 2.2129 | 0.8207                  |
+| 5.2941 | 360  | 1.3909        | -      | -                       |
+| 5.4412 | 370  | 1.4116        | -      | -                       |
+| 5.5882 | 380  | 1.52          | -      | -                       |
+| 5.7353 | 390  | 1.3644        | -      | -                       |
+| 5.8824 | 400  | 1.3016        | 2.1699 | 0.8266                  |
+| 6.0294 | 410  | 1.4435        | -      | -                       |
+| 6.1765 | 420  | 1.3112        | -      | -                       |
+| 6.3235 | 430  | 1.4056        | -      | -                       |
+| 6.4706 | 440  | 1.4541        | -      | -                       |
+| 6.6176 | 450  | 1.3312        | 2.1486 | 0.8224                  |
+| 6.7647 | 460  | 1.2879        | -      | -                       |
+| 6.9118 | 470  | 1.227         | -      | -                       |
+| 7.0588 | 480  | 1.3834        | -      | -                       |
+| 7.2059 | 490  | 1.3242        | -      | -                       |
+| 7.3529 | 500  | 1.3756        | 2.1507 | 0.8274                  |
+| 7.5    | 510  | 1.2872        | -      | -                       |
+| 7.6471 | 520  | 1.3288        | -      | -                       |
+| 7.7941 | 530  | 1.2689        | -      | -                       |
+| 7.9412 | 540  | 1.3102        | -      | -                       |
+| 8.0882 | 550  | 1.2929        | 2.1355 | 0.8207                  |
+| 8.2353 | 560  | 1.2511        | -      | -                       |
+| 8.3824 | 570  | 1.1849        | -      | -                       |
+| 8.5294 | 580  | 1.2774        | -      | -                       |
+| 8.6765 | 590  | 1.1923        | -      | -                       |
+| 8.8235 | 600  | 1.1927        | 2.1111 | 0.8283                  |
+| 8.9706 | 610  | 1.2556        | -      | -                       |
+| 9.1176 | 620  | 1.2767        | -      | -                       |
+| 9.2647 | 630  | 1.1082        | -      | -                       |
+| 9.4118 | 640  | 1.3077        | -      | -                       |
+| 9.5588 | 650  | 1.1435        | 2.0922 | 0.8316                  |
+| 9.7059 | 660  | 1.1888        | -      | -                       |
+| 9.8529 | 670  | 1.2123        | -      | -                       |
+| 10.0   | 680  | 1.2554        | -      | -                       |
+
+
+### Framework Versions
+- Python: 3.8.18
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 1.13.1+cu117
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- 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}
+}
+```
+
+<!--
+## 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 +1,26 @@
+{
+  "_name_or_path": "../models/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-10e",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.2",
+    "pytorch": "1.13.1+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46744ec43fabf3a3be9d3d4f33812a707b236c0a82b52d62396cd27f1d280120
+size 437951328

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
+}

special_tokens_map.json

@@ -0,0 +1,44 @@
+{
+  "additional_special_tokens": [
+    "[PAD]",
+    "[UNK]",
+    "[CLS]",
+    "[SEP]",
+    "[MASK]"
+  ],
+  "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 +1,66 @@
+{
+  "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
+    }
+  },
+  "additional_special_tokens": [
+    "[PAD]",
+    "[UNK]",
+    "[CLS]",
+    "[SEP]",
+    "[MASK]"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}