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

@@ -0,0 +1,589 @@
+---
+base_model: dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e
+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 based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: beatai dev
+      type: beatai-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.813973063973064
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.22727272727272727
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8198653198653199
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8156565656565656
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8198653198653199
+      name: Max Accuracy
+---
+
+# SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e). 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:** [dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e) <!-- at revision 86e3b91181f7c10aa5a92184184dc50f0f25aa57 -->
+- **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-cosine-80e")
+# 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** | **0.814** |
+| dot_accuracy        | 0.2273    |
+| manhattan_accuracy  | 0.8199    |
+| euclidean_accuracy  | 0.8157    |
+| max_accuracy        | 0.8199    |
+
+<!--
+## 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`: 5e-07
+- `weight_decay`: 0.01
+- `num_train_epochs`: 30
+- `lr_scheduler_type`: constant
+- `bf16`: True
+- `dataloader_drop_last`: True
+- `resume_from_checkpoint`: 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`: 5e-07
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 30
+- `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`: True
+- `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
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step | Training Loss | loss   | beatai-dev_cosine_accuracy |
+|:-------:|:----:|:-------------:|:------:|:--------------------------:|
+| 0       | 0    | -             | -      | 0.7904                     |
+| 0.1471  | 10   | 0.0721        | -      | -                          |
+| 0.2941  | 20   | 0.0708        | -      | -                          |
+| 0.4412  | 30   | 0.0736        | -      | -                          |
+| 0.5882  | 40   | 0.0704        | -      | -                          |
+| 0.7353  | 50   | 0.0732        | 0.0971 | 0.7929                     |
+| 0.8824  | 60   | 0.0716        | -      | -                          |
+| 1.0294  | 70   | 0.0665        | -      | -                          |
+| 1.1765  | 80   | 0.0698        | -      | -                          |
+| 1.3235  | 90   | 0.0699        | -      | -                          |
+| 1.4706  | 100  | 0.0691        | 0.0968 | 0.7912                     |
+| 1.6176  | 110  | 0.0687        | -      | -                          |
+| 1.7647  | 120  | 0.0701        | -      | -                          |
+| 1.9118  | 130  | 0.0689        | -      | -                          |
+| 2.0588  | 140  | 0.0696        | -      | -                          |
+| 2.2059  | 150  | 0.071         | 0.0966 | 0.7929                     |
+| 2.3529  | 160  | 0.078         | -      | -                          |
+| 2.5     | 170  | 0.0675        | -      | -                          |
+| 2.6471  | 180  | 0.065         | -      | -                          |
+| 2.7941  | 190  | 0.0684        | -      | -                          |
+| 2.9412  | 200  | 0.0689        | 0.0963 | 0.7938                     |
+| 3.0882  | 210  | 0.0736        | -      | -                          |
+| 3.2353  | 220  | 0.0684        | -      | -                          |
+| 3.3824  | 230  | 0.0669        | -      | -                          |
+| 3.5294  | 240  | 0.0688        | -      | -                          |
+| 3.6765  | 250  | 0.0678        | 0.0959 | 0.7963                     |
+| 3.8235  | 260  | 0.0682        | -      | -                          |
+| 3.9706  | 270  | 0.0678        | -      | -                          |
+| 4.1176  | 280  | 0.0686        | -      | -                          |
+| 4.2647  | 290  | 0.0664        | -      | -                          |
+| 4.4118  | 300  | 0.0703        | 0.0957 | 0.7980                     |
+| 4.5588  | 310  | 0.065         | -      | -                          |
+| 4.7059  | 320  | 0.0719        | -      | -                          |
+| 4.8529  | 330  | 0.0685        | -      | -                          |
+| 5.0     | 340  | 0.0639        | -      | -                          |
+| 5.1471  | 350  | 0.0667        | 0.0957 | 0.7971                     |
+| 5.2941  | 360  | 0.0661        | -      | -                          |
+| 5.4412  | 370  | 0.0678        | -      | -                          |
+| 5.5882  | 380  | 0.0725        | -      | -                          |
+| 5.7353  | 390  | 0.0655        | -      | -                          |
+| 5.8824  | 400  | 0.0649        | 0.0953 | 0.7980                     |
+| 6.0294  | 410  | 0.0661        | -      | -                          |
+| 6.1765  | 420  | 0.0662        | -      | -                          |
+| 6.3235  | 430  | 0.0671        | -      | -                          |
+| 6.4706  | 440  | 0.0698        | -      | -                          |
+| 6.6176  | 450  | 0.0636        | 0.0951 | 0.7980                     |
+| 6.7647  | 460  | 0.0644        | -      | -                          |
+| 6.9118  | 470  | 0.0633        | -      | -                          |
+| 7.0588  | 480  | 0.0679        | -      | -                          |
+| 7.2059  | 490  | 0.067         | -      | -                          |
+| 7.3529  | 500  | 0.0713        | 0.0948 | 0.7963                     |
+| 7.5     | 510  | 0.0677        | -      | -                          |
+| 7.6471  | 520  | 0.0666        | -      | -                          |
+| 7.7941  | 530  | 0.065         | -      | -                          |
+| 7.9412  | 540  | 0.0665        | -      | -                          |
+| 8.0882  | 550  | 0.0656        | 0.0946 | 0.7963                     |
+| 8.2353  | 560  | 0.0649        | -      | -                          |
+| 8.3824  | 570  | 0.0649        | -      | -                          |
+| 8.5294  | 580  | 0.0653        | -      | -                          |
+| 8.6765  | 590  | 0.0648        | -      | -                          |
+| 8.8235  | 600  | 0.0622        | 0.0944 | 0.7946                     |
+| 8.9706  | 610  | 0.0689        | -      | -                          |
+| 9.1176  | 620  | 0.0711        | -      | -                          |
+| 9.2647  | 630  | 0.0611        | -      | -                          |
+| 9.4118  | 640  | 0.0697        | -      | -                          |
+| 9.5588  | 650  | 0.0645        | 0.0942 | 0.7963                     |
+| 9.7059  | 660  | 0.0639        | -      | -                          |
+| 9.8529  | 670  | 0.0643        | -      | -                          |
+| 10.0    | 680  | 0.0644        | -      | -                          |
+| 10.1471 | 690  | 0.0599        | -      | -                          |
+| 10.2941 | 700  | 0.0723        | 0.0940 | 0.7955                     |
+| 10.4412 | 710  | 0.0652        | -      | -                          |
+| 10.5882 | 720  | 0.0646        | -      | -                          |
+| 10.7353 | 730  | 0.0602        | -      | -                          |
+| 10.8824 | 740  | 0.0644        | -      | -                          |
+| 11.0294 | 750  | 0.066         | 0.0938 | 0.7971                     |
+| 11.1765 | 760  | 0.0624        | -      | -                          |
+| 11.3235 | 770  | 0.0652        | -      | -                          |
+| 11.4706 | 780  | 0.0649        | -      | -                          |
+| 11.6176 | 790  | 0.0624        | -      | -                          |
+| 11.7647 | 800  | 0.0626        | 0.0937 | 0.7988                     |
+| 11.9118 | 810  | 0.0635        | -      | -                          |
+| 12.0588 | 820  | 0.0643        | -      | -                          |
+| 12.2059 | 830  | 0.0663        | -      | -                          |
+| 12.3529 | 840  | 0.0641        | -      | -                          |
+| 12.5    | 850  | 0.0614        | 0.0933 | 0.8005                     |
+| 12.6471 | 860  | 0.0613        | -      | -                          |
+| 12.7941 | 870  | 0.0648        | -      | -                          |
+| 12.9412 | 880  | 0.065         | -      | -                          |
+| 13.0882 | 890  | 0.0589        | -      | -                          |
+| 13.2353 | 900  | 0.0632        | 0.0931 | 0.7997                     |
+| 13.3824 | 910  | 0.0649        | -      | -                          |
+| 13.5294 | 920  | 0.0612        | -      | -                          |
+| 13.6765 | 930  | 0.0634        | -      | -                          |
+| 13.8235 | 940  | 0.0637        | -      | -                          |
+| 13.9706 | 950  | 0.0626        | 0.0930 | 0.7997                     |
+| 14.1176 | 960  | 0.0593        | -      | -                          |
+| 14.2647 | 970  | 0.0662        | -      | -                          |
+| 14.4118 | 980  | 0.0644        | -      | -                          |
+| 14.5588 | 990  | 0.0582        | -      | -                          |
+| 14.7059 | 1000 | 0.0626        | 0.0927 | 0.8013                     |
+| 14.8529 | 1010 | 0.0605        | -      | -                          |
+| 15.0    | 1020 | 0.0615        | -      | -                          |
+| 15.1471 | 1030 | 0.0676        | -      | -                          |
+| 15.2941 | 1040 | 0.0633        | -      | -                          |
+| 15.4412 | 1050 | 0.06          | 0.0927 | 0.8047                     |
+| 15.5882 | 1060 | 0.0572        | -      | -                          |
+| 15.7353 | 1070 | 0.0579        | -      | -                          |
+| 15.8824 | 1080 | 0.0594        | -      | -                          |
+| 16.0294 | 1090 | 0.063         | -      | -                          |
+| 16.1765 | 1100 | 0.0581        | 0.0927 | 0.8030                     |
+| 16.3235 | 1110 | 0.0564        | -      | -                          |
+| 16.4706 | 1120 | 0.0632        | -      | -                          |
+| 16.6176 | 1130 | 0.065         | -      | -                          |
+| 16.7647 | 1140 | 0.0602        | -      | -                          |
+| 16.9118 | 1150 | 0.0581        | 0.0926 | 0.8039                     |
+| 17.0588 | 1160 | 0.0623        | -      | -                          |
+| 17.2059 | 1170 | 0.06          | -      | -                          |
+| 17.3529 | 1180 | 0.0562        | -      | -                          |
+| 17.5    | 1190 | 0.0627        | -      | -                          |
+| 17.6471 | 1200 | 0.056         | 0.0924 | 0.8013                     |
+| 17.7941 | 1210 | 0.0586        | -      | -                          |
+| 17.9412 | 1220 | 0.0576        | -      | -                          |
+| 18.0882 | 1230 | 0.056         | -      | -                          |
+| 18.2353 | 1240 | 0.0611        | -      | -                          |
+| 18.3824 | 1250 | 0.0551        | 0.0922 | 0.8047                     |
+| 18.5294 | 1260 | 0.058         | -      | -                          |
+| 18.6765 | 1270 | 0.0571        | -      | -                          |
+| 18.8235 | 1280 | 0.0616        | -      | -                          |
+| 18.9706 | 1290 | 0.0599        | -      | -                          |
+| 19.1176 | 1300 | 0.0604        | 0.0920 | 0.8081                     |
+| 19.2647 | 1310 | 0.0633        | -      | -                          |
+| 19.4118 | 1320 | 0.0573        | -      | -                          |
+| 19.5588 | 1330 | 0.0549        | -      | -                          |
+| 19.7059 | 1340 | 0.0591        | -      | -                          |
+| 19.8529 | 1350 | 0.0585        | 0.0918 | 0.8089                     |
+| 20.0    | 1360 | 0.057         | -      | -                          |
+| 20.1471 | 1370 | 0.057         | -      | -                          |
+| 20.2941 | 1380 | 0.0625        | -      | -                          |
+| 20.4412 | 1390 | 0.0589        | -      | -                          |
+| 20.5882 | 1400 | 0.0577        | 0.0918 | 0.8098                     |
+| 20.7353 | 1410 | 0.0583        | -      | -                          |
+| 20.8824 | 1420 | 0.0567        | -      | -                          |
+| 21.0294 | 1430 | 0.0619        | -      | -                          |
+| 21.1765 | 1440 | 0.0572        | -      | -                          |
+| 21.3235 | 1450 | 0.0594        | 0.0917 | 0.8123                     |
+| 21.4706 | 1460 | 0.0567        | -      | -                          |
+| 21.6176 | 1470 | 0.0611        | -      | -                          |
+| 21.7647 | 1480 | 0.0533        | -      | -                          |
+| 21.9118 | 1490 | 0.0595        | -      | -                          |
+| 22.0588 | 1500 | 0.0521        | 0.0913 | 0.8114                     |
+| 22.2059 | 1510 | 0.0586        | -      | -                          |
+| 22.3529 | 1520 | 0.0603        | -      | -                          |
+| 22.5    | 1530 | 0.0601        | -      | -                          |
+| 22.6471 | 1540 | 0.0567        | -      | -                          |
+| 22.7941 | 1550 | 0.0551        | 0.0911 | 0.8114                     |
+| 22.9412 | 1560 | 0.0542        | -      | -                          |
+| 23.0882 | 1570 | 0.057         | -      | -                          |
+| 23.2353 | 1580 | 0.0541        | -      | -                          |
+| 23.3824 | 1590 | 0.0586        | -      | -                          |
+| 23.5294 | 1600 | 0.0573        | 0.0912 | 0.8106                     |
+| 23.6765 | 1610 | 0.0543        | -      | -                          |
+| 23.8235 | 1620 | 0.0578        | -      | -                          |
+| 23.9706 | 1630 | 0.0563        | -      | -                          |
+| 24.1176 | 1640 | 0.0549        | -      | -                          |
+| 24.2647 | 1650 | 0.0549        | 0.0909 | 0.8140                     |
+| 24.4118 | 1660 | 0.056         | -      | -                          |
+| 24.5588 | 1670 | 0.0599        | -      | -                          |
+| 24.7059 | 1680 | 0.0543        | -      | -                          |
+| 24.8529 | 1690 | 0.0547        | -      | -                          |
+| 25.0    | 1700 | 0.0575        | 0.0906 | 0.8114                     |
+| 25.1471 | 1710 | 0.0544        | -      | -                          |
+| 25.2941 | 1720 | 0.0574        | -      | -                          |
+| 25.4412 | 1730 | 0.0565        | -      | -                          |
+| 25.5882 | 1740 | 0.0587        | -      | -                          |
+| 25.7353 | 1750 | 0.0559        | 0.0905 | 0.8157                     |
+| 25.8824 | 1760 | 0.0551        | -      | -                          |
+| 26.0294 | 1770 | 0.0569        | -      | -                          |
+| 26.1765 | 1780 | 0.0516        | -      | -                          |
+| 26.3235 | 1790 | 0.0561        | -      | -                          |
+| 26.4706 | 1800 | 0.0567        | 0.0906 | 0.8165                     |
+| 26.6176 | 1810 | 0.0599        | -      | -                          |
+| 26.7647 | 1820 | 0.0577        | -      | -                          |
+| 26.9118 | 1830 | 0.0532        | -      | -                          |
+| 27.0588 | 1840 | 0.0554        | -      | -                          |
+| 27.2059 | 1850 | 0.0579        | 0.0906 | 0.8123                     |
+| 27.3529 | 1860 | 0.0532        | -      | -                          |
+| 27.5    | 1870 | 0.0493        | -      | -                          |
+| 27.6471 | 1880 | 0.0552        | -      | -                          |
+| 27.7941 | 1890 | 0.0532        | -      | -                          |
+| 27.9412 | 1900 | 0.0569        | 0.0904 | 0.8089                     |
+| 28.0882 | 1910 | 0.0568        | -      | -                          |
+| 28.2353 | 1920 | 0.052         | -      | -                          |
+| 28.3824 | 1930 | 0.0555        | -      | -                          |
+| 28.5294 | 1940 | 0.0563        | -      | -                          |
+| 28.6765 | 1950 | 0.0555        | 0.0903 | 0.8140                     |
+| 28.8235 | 1960 | 0.0535        | -      | -                          |
+| 28.9706 | 1970 | 0.0525        | -      | -                          |
+| 29.1176 | 1980 | 0.0566        | -      | -                          |
+| 29.2647 | 1990 | 0.0562        | -      | -                          |
+| 29.4118 | 2000 | 0.0547        | 0.0902 | 0.8140                     |
+| 29.5588 | 2010 | 0.0495        | -      | -                          |
+| 29.7059 | 2020 | 0.0532        | -      | -                          |
+| 29.8529 | 2030 | 0.0553        | -      | -                          |
+| 30.0    | 2040 | 0.0544        | -      | -                          |
+
+</details>
+
+### Framework Versions
+- Python: 3.8.18
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.1
+- PyTorch: 1.13.1+cu117
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- Tokenizers: 0.20.0
+
+## 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": "dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e",
+  "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": "4.45.1",
+  "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": "4.45.1",
+    "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:a8484d101ae363b9be14f2fbaa7e43630f5222d0528ea4c7a561491b6086bceb
+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]"
+}