Upload checkpoint-54/README.md

checkpoint-54/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:849
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L12-v2
+widget:
+- source_sentence: Graphic designer who specializes in creating visual content for
+    brands, including logos, marketing materials, and user interfaces. Focuses on
+    aesthetics, user experience, and brand identity.
+  sentences:
+  - 'user_1: I''m looking to refresh my company''s brand image but don''t know where
+    to start.
+
+    user_2: You should consult a brand manager.'
+  - 'user_1: I need help designing a logo for my new business.
+
+    user_2: Have you thought about hiring a graphic designer?
+
+    user_1: Yes, I want something that really represents my brand.'
+  - 'user_1: My car''s making a weird noise, and I don''t know what to do.
+
+    user_2: You should take it to a mechanic.'
+- source_sentence: Nutritionist who specializes in dietary planning and nutritional
+    counseling. Helps clients achieve their health goals through personalized meal
+    plans and education.
+  sentences:
+  - 'user_1: I''m trying to lose weight but I don''t know what to eat.
+
+    user_2: Have you considered talking to a nutritionist?'
+  - 'user_1: Our database is running slow, and I don''t know why.
+
+    user_2: Have you checked the indexing?'
+  - 'user_1: I need help fixing my car''s engine; it''s making a weird noise.
+
+    user_2: Have you checked the oil level?'
+- source_sentence: 'user_2: Sure, what problem are you working on?'
+  sentences:
+  - Gardening expert specializing in vegetable gardening techniques and plant care.
+  - Event planner focusing on corporate events and wedding coordination.
+  - Math tutor specializing in teaching and clarifying mathematical concepts and problem-solving.
+- source_sentence: 'user_2: Have you thought about getting some storage bins?'
+  sentences:
+  - Web developer focused on software engineering and application design.
+  - Professional organizer specializing in home organization and decluttering strategies.
+  - Pet behavior specialist who provides advice on dog breeds and training for small
+    living spaces.
+- source_sentence: 'user_1: Maybe the national parks, I want to see some nature.'
+  sentences:
+  - Mental health counselor specializing in stress management and coping strategies.
+  - Data analyst focusing on market trends and business intelligence.
+  - Travel consultant specializing in road trip planning and national park itineraries.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) on the semantic_triplets_round1 and inverse_semantic_triplets datasets. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision c004d8e3e901237d8fa7e9fff12774962e391ce5 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - semantic_triplets_round1
+    - inverse_semantic_triplets
+<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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})
+  (2): Normalize()
+)
+```
+
+## 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 = [
+    'user_1: Maybe the national parks, I want to see some nature.',
+    'Travel consultant specializing in road trip planning and national park itineraries.',
+    'Data analyst focusing on market trends and business intelligence.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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 Datasets
+
+#### semantic_triplets_round1
+
+* Dataset: semantic_triplets_round1
+* Size: 422 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 422 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 17.44 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 14.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.49 tokens</li><li>max: 20 tokens</li></ul> |
+* Samples:
+  | anchor                                                                          | positive                                                                                                 | negative                                                                                        |
+  |:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
+  | <code>user_1: Can anyone recommend a good app for tracking my expenses?</code>  | <code>Personal finance advisor specializing in budgeting tools and expense tracking applications.</code> | <code>Fitness instructor focusing on workout plans and nutrition.</code>                        |
+  | <code>user_1: Can anyone recommend a good workout routine for beginners?</code> | <code>Fitness trainer who specializes in creating beginner workout plans and exercise coaching.</code>   | <code>Financial advisor focused on investment strategies and retirement planning.</code>        |
+  | <code>user_2: What kind of vegetables are you thinking of planting?</code>      | <code>Gardening expert who provides guidance on vegetable gardening techniques and plant care.</code>    | <code>Investment advisor specializing in stock market strategies and financial planning.</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"
+  }
+  ```
+
+#### inverse_semantic_triplets
+
+* Dataset: inverse_semantic_triplets
+* Size: 427 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 427 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 18 tokens</li><li>mean: 28.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 40.04 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 27.66 tokens</li><li>max: 62 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                           | positive                                                                                                                                                                                                                                                   | negative                                                                                                                     |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
+  | <code>UX researcher specializing in user experience design and user testing. Conducts research to understand user needs and improve product usability.</code>                                                                    | <code>user_1: I'm looking for ways to improve the usability of our app.<br>user_2: Have you considered conducting user interviews?</code>                                                                                                                  | <code>user_1: I need to plan a trip to Europe next summer.<br>user_2: What countries are you thinking about visiting?</code> |
+  | <code>Software developer specializing in web applications, proficient in various programming languages and frameworks. I design, develop, and maintain software solutions, focusing on user experience and functionality.</code> | <code>user_1: I'm trying to build a web application, but I'm stuck on how to integrate the backend with the frontend.<br>user_2: What technologies are you using for both?<br>user_1: I’m using Node.js for the backend and React for the frontend.</code> | <code>user_1: I'm looking for a good recipe for chocolate chip cookies.<br>user_2: I can share my favorite one!</code>       |
+  | <code>Marketing strategist who focuses on developing comprehensive marketing plans to drive brand engagement and sales growth. Specializes in digital marketing and content strategy.</code>                                     | <code>user_1: I'm launching a new product and need a marketing strategy.<br>user_2: Have you set any goals for your campaign?</code>                                                                                                                       | <code>user_1: I'm looking for a new pair of running shoes.<br>user_2: What brand do you prefer?</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"
+  }
+  ```
+
+### Evaluation Datasets
+
+#### semantic_triplets_round1
+
+* Dataset: semantic_triplets_round1
+* Size: 47 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 47 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 17.87 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 14.32 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 12.49 tokens</li><li>max: 16 tokens</li></ul> |
+* Samples:
+  | anchor                                                                            | positive                                                                                     | negative                                                                                       |
+  |:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | <code>user_1: What's the best way to train my puppy to stop barking?</code>       | <code>Dog training specialist focused on behavioral issues and obedience training.</code>    | <code>Financial advisor who specializes in investment strategies and wealth management.</code> |
+  | <code>user_2: What vegetables do you want to grow?</code>                         | <code>Gardening expert specializing in vegetable gardening and sustainable practices.</code> | <code>Real estate agent focusing on home buying and selling.</code>                            |
+  | <code>user_1: Anyone have tips on how to improve my running time for a 5k?</code> | <code>Running coach specializing in training plans and performance improvement.</code>       | <code>Financial advisor focusing on investment strategies and retirement planning.</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"
+  }
+  ```
+
+#### inverse_semantic_triplets
+
+* Dataset: inverse_semantic_triplets
+* Size: 48 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 48 samples:
+  |         | anchor                                                                             | positive                                                                           | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 20 tokens</li><li>mean: 28.42 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 39.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.4 tokens</li><li>max: 52 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                            | positive                                                                                                                                                                                              | negative                                                                                                                      |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Graphic designer who specializes in creating visual content for brands, including logos, marketing materials, and user interfaces. Focuses on aesthetics, user experience, and brand identity.</code>                       | <code>user_1: I need help designing a logo for my new business.<br>user_2: Have you thought about hiring a graphic designer?<br>user_1: Yes, I want something that really represents my brand.</code> | <code>user_1: My car's making a weird noise, and I don't know what to do.<br>user_2: You should take it to a mechanic.</code> |
+  | <code>Physical therapist specializing in rehabilitation for sports injuries, pain management, and improving mobility through tailored exercise programs.</code>                                                                   | <code>user_1: I twisted my ankle playing basketball, and it's really swollen.<br>user_2: Have you seen a doctor about it?</code>                                                                      | <code>user_1: I'm thinking of redecorating my living room.<br>user_2: What style are you going for?</code>                    |
+  | <code>An accountant who specializes in financial record-keeping, tax preparation, and business consulting. Provides services to help clients manage their finances effectively and ensure compliance with tax regulations.</code> | <code>user_1: I need help with my taxes this year.<br>user_2: Are you looking for someone to prepare them for you?</code>                                                                             | <code>user_1: I'm thinking about getting a puppy.</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"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### 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`: 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`: 1
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Framework Versions
+- Python: 3.12.9
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.4
+- PyTorch: 2.7.1
+- Accelerate: 1.8.1
+- 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",
+}
+```
+
+#### 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.*
+-->