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README.md

@@ -1,3 +1,423 @@
----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:16002
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: microsoft/mpnet-base
+widget:
+- source_sentence: F*CK Him! — Nice Girls Always Finish Single —  “A guide for sassy
+    women who want to get back in control of their love life” by Brian Nox
+  sentences:
+  - Books on Innovation
+  - Books on Dating
+  - Books on Entrepreneurship
+- source_sentence: It Doesn't Have to Be Crazy at Work by Jason Fried, David Heinemeier
+    Hansson
+  sentences:
+  - Books on Psychology
+  - Books on Communication
+  - Books on Business Strategy
+- source_sentence: Harvard Business Review Guide to Finance Basics for Managers by
+    Harvard Business School Press
+  sentences:
+  - Books on Management
+  - Books on Sociology
+  - Books on Feng Shui
+- source_sentence: 'Captivate: The Science of Succeeding with People by Vanessa Van
+    Edwards'
+  sentences:
+  - Books on Management
+  - Books on Leadership
+  - Books on Ethical Dilemmas
+- source_sentence: 'Who: The A Method for Hiring by Geoff Smart and Randy Street'
+  sentences:
+  - Books on Habits
+  - Books on Communication
+  - Books on Artificial Intelligence
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on microsoft/mpnet-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the csv dataset. 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **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: MPNetModel 
+  (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 = [
+    'Who: The A Method for Hiring by Geoff Smart and Randy Street',
+    'Books on Communication',
+    'Books on Artificial Intelligence',
+]
+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.*
+-->
+
+<!--
+## 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
+
+#### csv
+
+* Dataset: csv
+* Size: 16,002 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                          |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 21.88 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.83 tokens</li><li>max: 9 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                               | positive                             |
+  |:-------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|
+  | <code>Millionaire Success Habits: The Gateway to Wealth & Prosperity by Dean Graziosi</code>                                         | <code>Books on Wealth</code>         |
+  | <code>Thinking With Type: A Critical Guide For Designers, Writers, Editors, & Students by Ellen Lupton</code>                        | <code>Books on Graphic Design</code> |
+  | <code>The Happiest Baby on the Block: The New Way to Calm Crying and Help Your Newborn Baby Sleep Longer by Harvey Karp, M.D.</code> | <code>Books on Pregnancy</code>      |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### csv
+
+* Dataset: csv
+* Size: 1,778 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 21.99 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 5.79 tokens</li><li>max: 10 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                     | positive                               |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------|
+  | <code>Crying in H Mart: A Memoir by Michelle Zauner</code>                                                                                                 | <code>Books about Family</code>        |
+  | <code>Starting a Successful Blog when You Have NO CLUE! by Gundi Gabrielle</code>                                                                          | <code>Books on Content Creation</code> |
+  | <code>The XX Brain: The Groundbreaking Science Empowering Women to Maximize Cognitive Health and Prevent Alzheimer's Disease by Lisa Mosconi, Ph.D.</code> | <code>Books on Health</code>           |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0999 | 100  | 3.0908        |
+| 0.1998 | 200  | 2.3816        |
+| 0.2997 | 300  | 2.1439        |
+| 0.3996 | 400  | 1.9773        |
+| 0.4995 | 500  | 1.9802        |
+| 0.5994 | 600  | 1.9266        |
+| 0.6993 | 700  | 1.8154        |
+| 0.7992 | 800  | 1.792         |
+| 0.8991 | 900  | 1.7716        |
+| 0.9990 | 1000 | 1.7447        |
+| 1.0989 | 1100 | 1.6836        |
+| 1.1988 | 1200 | 1.635         |
+| 1.2987 | 1300 | 1.555         |
+| 1.3986 | 1400 | 1.6318        |
+| 1.4985 | 1500 | 1.6539        |
+| 1.5984 | 1600 | 1.5797        |
+| 1.6983 | 1700 | 1.5831        |
+| 1.7982 | 1800 | 1.5727        |
+| 1.8981 | 1900 | 1.5969        |
+| 1.9980 | 2000 | 1.5202        |
+| 2.0979 | 2100 | 1.4787        |
+| 2.1978 | 2200 | 1.4702        |
+| 2.2977 | 2300 | 1.4389        |
+| 2.3976 | 2400 | 1.4196        |
+| 2.4975 | 2500 | 1.4352        |
+| 2.5974 | 2600 | 1.395         |
+| 2.6973 | 2700 | 1.462         |
+| 2.7972 | 2800 | 1.4439        |
+| 2.8971 | 2900 | 1.4478        |
+| 2.9970 | 3000 | 1.4617        |
+| 3.0969 | 3100 | 1.3563        |
+| 3.1968 | 3200 | 1.4004        |
+| 3.2967 | 3300 | 1.3557        |
+| 3.3966 | 3400 | 1.3226        |
+| 3.4965 | 3500 | 1.3516        |
+| 3.5964 | 3600 | 1.3219        |
+| 3.6963 | 3700 | 1.4065        |
+| 3.7962 | 3800 | 1.3987        |
+| 3.8961 | 3900 | 1.3364        |
+| 3.9960 | 4000 | 1.3198        |
+| 4.0959 | 4100 | 1.2749        |
+| 4.1958 | 4200 | 1.2973        |
+| 4.2957 | 4300 | 1.2335        |
+| 4.3956 | 4400 | 1.2816        |
+| 4.4955 | 4500 | 1.2963        |
+| 4.5954 | 4600 | 1.2932        |
+| 4.6953 | 4700 | 1.2579        |
+| 4.7952 | 4800 | 1.3312        |
+| 4.8951 | 4900 | 1.262         |
+| 4.9950 | 5000 | 1.3559        |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 4.1.0
+- Transformers: 4.53.0.dev0
+- PyTorch: 2.7.1+cu126
+- Accelerate: 1.7.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.0.dev0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.53.0.dev0",
+    "pytorch": "2.7.1+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

optimizer.pt

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rng_state.pth

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scheduler.pt

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sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
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+    "lstrip": false,
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+    "single_word": false
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+  "mask_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
+      "special": true
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+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}

trainer_state.json

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