Upload folder using huggingface_hub

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- autotrain
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: 'search_query: i love autotrain'
+  sentences:
+  - 'search_query: huggingface auto train'
+  - 'search_query: hugging face auto train'
+  - 'search_query: i love autotrain'
+pipeline_tag: sentence-similarity
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Sentence Transformers
+
+## Validation Metrics
+loss: 0.3288682699203491
+
+runtime: 6.5691
+
+samples_per_second: 42.776
+
+steps_per_second: 2.74
+
+: 3.0
+
+## 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 Hugging Face Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'search_query: autotrain',
+    'search_query: auto train',
+    'search_query: i love autotrain',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+```

checkpoint-423/1_Pooling/config.json

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+{
+  "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
+}

checkpoint-423/README.md

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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1121
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: How is DNA used in forensic science?
+  sentences:
+  - 'Cellular respiration consists of three main stages: glycolysis, the Krebs cycle
+    (citric acid cycle), and the electron transport chain.'
+  - DNA is used in forensic science to identify individuals and establish connections
+    between suspects and crime scenes through DNA profiling. It provides unique genetic
+    information that can match evidence with individuals.
+  - ' "Apologies, but I am here to assist with educational inquiries only."'
+- source_sentence: Which star is closest to Earth?
+  sentences:
+  - ' "A digital thermometer measures temperature by using electronic sensors, such
+    as thermistors or thermocouples, which change their resistance or voltage in response
+    to temperature changes. The thermometer then converts these readings into a digital
+    display."'
+  - The closest star to Earth, other than the Sun, is Proxima Centauri.
+  - ' "Apologies, but I am here to assist with educational inquiries only."'
+- source_sentence: What is the significance of the concept of an interpolation method?
+  sentences:
+  - Eigenvalue decomposition is a method of breaking down a matrix into its eigenvalues
+    and eigenvectors, used in linear algebra to analyze and simplify matrix operations.
+  - Enzymes are biological catalysts that speed up chemical reactions in living organisms
+    by lowering the activation energy required for the reactions to occur. They are
+    crucial for processes such as digestion, metabolism, and cellular functions.
+  - An interpolation method is a technique used to estimate values between known data
+    points, used in numerical analysis and data fitting to approximate functions and
+    curves.
+- source_sentence: What is the concept of a Differential Form in calculus?
+  sentences:
+  - ' "Apologies, but I am here to assist with educational inquiries only."'
+  - ' "Apologies, but I am here to assist with educational inquiries only."'
+  - A differential form is a mathematical object that generalizes functions and vectors,
+    used in multivariable calculus and differential geometry.
+- source_sentence: Do you know any good methods for managing personal effectiveness?
+  sentences:
+  - ' "Apologies, but I am here to assist with educational inquiries only."'
+  - ' "A thermocouple measures temperature by generating a voltage based on the temperature
+    difference between two different types of metal wires joined at one end. The voltage
+    is proportional to the temperature difference."'
+  - A principal component is a direction in which the data varies the most, used in
+    principal component analysis to reduce dimensionality and identify key features
+    in the data.
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 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': 256, '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 = [
+    'Do you know any good methods for managing personal effectiveness?',
+    ' "Apologies, but I am here to assist with educational inquiries only."',
+    ' "A thermocouple measures temperature by generating a voltage based on the temperature difference between two different types of metal wires joined at one end. The voltage is proportional to the temperature difference."',
+]
+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 Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,121 training samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | answer                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 12.37 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.06 tokens</li><li>max: 81 tokens</li></ul> |
+* Samples:
+  | query                                                           | answer                                                                                                                                                                              |
+  |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Do you have any tips for managing work challenges?</code> | <code> "Apologies, but I am here to assist with educational inquiries only."</code>                                                                                                 |
+  | <code>Do you have any tips for improving focus?</code>          | <code> "Apologies, but I am here to assist with educational inquiries only."</code>                                                                                                 |
+  | <code>What is the concept of a random walk?</code>              | <code>A random walk is a stochastic process that describes a path consisting of a succession of random steps, used to model various phenomena in probability and statistics.</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 Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 281 evaluation samples
+* Columns: <code>query</code> and <code>answer</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | answer                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 12.22 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 30.74 tokens</li><li>max: 69 tokens</li></ul> |
+* Samples:
+  | query                                                                             | answer                                                                                                                                                                                                                                   |
+  |:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How does a thermocouple measure temperature?</code>                         | <code> "A thermocouple measures temperature by generating a voltage based on the temperature difference between two different types of metal wires joined at one end. The voltage is proportional to the temperature difference."</code> |
+  | <code>What is the concept of a Riemann-Roch theorem in algebraic geometry?</code> | <code>The Riemann-Roch theorem provides a formula for the dimension of the space of meromorphic functions on a compact Riemann surface, used in algebraic geometry.</code>                                                               |
+  | <code>Can you give me some relaxation tips?</code>                                | <code> "Apologies, but I am here to assist with educational inquiries only."</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`: epoch
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 3e-05
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `ddp_find_unused_parameters`: False
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `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`: 3e-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`: 3
+- `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`: True
+- `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`: True
+- `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`: False
+- `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   |
+|:------:|:----:|:-------------:|:------:|
+| 0.1773 | 25   | 0.4901        | -      |
+| 0.3546 | 50   | 0.2825        | -      |
+| 0.5319 | 75   | 0.2687        | -      |
+| 0.7092 | 100  | 0.3781        | -      |
+| 0.8865 | 125  | 0.2871        | -      |
+| 1.0    | 141  | -             | 0.3310 |
+| 1.0638 | 150  | 0.2355        | -      |
+| 1.2411 | 175  | 0.3316        | -      |
+| 1.4184 | 200  | 0.2709        | -      |
+| 1.5957 | 225  | 0.1992        | -      |
+| 1.7730 | 250  | 0.3492        | -      |
+| 1.9504 | 275  | 0.3475        | -      |
+| 2.0    | 282  | -             | 0.3293 |
+| 2.1277 | 300  | 0.2507        | -      |
+| 2.3050 | 325  | 0.211         | -      |
+| 2.4823 | 350  | 0.3757        | -      |
+| 2.6596 | 375  | 0.3035        | -      |
+| 2.8369 | 400  | 0.3962        | -      |
+| 3.0    | 423  | -             | 0.3289 |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.1
+- PyTorch: 2.3.0
+- Accelerate: 0.33.0
+- Datasets: 2.19.1
+- 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",
+}
+```
+
+#### 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.*
+-->

checkpoint-423/config.json

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+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-423/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.1",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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checkpoint-423/modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
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+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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checkpoint-423/sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

checkpoint-423/special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "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": {
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-423/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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