1HeroX1/hack_ai_embbedding_model

1_Pooling/config.json

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+{
+    "embedding_dimension": 1024,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:80
+- loss:CoSENTLoss
+base_model: abdeljalilELmajjodi/model
+widget:
+- source_sentence: A woman wearing all white and eating, walks next to a man holding
+    a briefcase.
+  sentences:
+  - A married couple is walking next to each other.
+  - The women do not care what clothes they wear.
+  - The diners are at a restaurant.
+- source_sentence: Two adults, one female in white, with shades and one male, gray
+    clothes, walking across a street, away from a eatery with a blurred image of a
+    dark colored red shirted person in the foreground.
+  sentences:
+  - A person is training his horse for a competition.
+  - The friends have just met for the first time in 20 years, and have had a great
+    time catching up.
+  - Two adults run across the street to get away from a red shirted person chasing
+    them.
+- source_sentence: A boy is jumping on skateboard in the middle of a red bridge.
+  sentences:
+  - The boy is wearing safety equipment.
+  - A woman eats ice cream walking down the sidewalk, and there is another woman in
+    front of her with a purse.
+  - The woman is cold.
+- source_sentence: High fashion ladies wait outside a tram beside a crowd of people
+    in the city.
+  sentences:
+  - The women enjoy having a good fashion sense.
+  - A woman in white.
+  - Olympic swimming.
+- source_sentence: Two women, holding food carryout containers, hug.
+  sentences:
+  - The people are related.
+  - A man and a woman walk down a crowded city street.
+  - The two sisters saw each other across the crowded diner and shared a hug, both
+    clutching their doggie bags.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer based on abdeljalilELmajjodi/model
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: pair score evaluator dev
+      type: pair-score-evaluator-dev
+    metrics:
+    - type: pearson_cosine
+      value: -0.35822503702342545
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: -0.4145870047803723
+      name: Spearman Cosine
+---
+
+# SentenceTransformer based on abdeljalilELmajjodi/model
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) on the all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) <!-- at revision 284169e2c18b482372374a251b8dc1e1756416de -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+- **Training Dataset:**
+    - all-nli
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
+  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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 = [
+    'Two women, holding food carryout containers, hug.',
+    'The two sisters saw each other across the crowded diner and shared a hug, both clutching their doggie bags.',
+    'The people are related.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.9138, 0.9157],
+#         [0.9138, 1.0000, 0.9143],
+#         [0.9157, 0.9143, 1.0000]])
+```
+<!--
+### 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
+
+#### Semantic Similarity
+
+* Dataset: `pair-score-evaluator-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value       |
+|:--------------------|:------------|
+| pearson_cosine      | -0.3582     |
+| **spearman_cosine** | **-0.4146** |
+
+<!--
+## 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
+
+#### all-nli
+
+* Dataset: all-nli
+* Size: 80 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 80 samples:
+  |         | sentence1                                                                          | sentence2                                                                         | score                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | float                                                          |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 25.79 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.45 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                            | sentence2                                           | score            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------|:-----------------|
+  | <code>A Little League team tries to catch a runner sliding into a base in an afternoon game.</code>                                  | <code>A team is trying to tag a runner out.</code>  | <code>1.0</code> |
+  | <code>Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.</code> | <code>The woman and man are outdoors.</code>        | <code>1.0</code> |
+  | <code>A couple play in the tide with their young son.</code>                                                                         | <code>The family is sitting down for dinner.</code> | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### all-nli
+
+* Dataset: all-nli
+* Size: 20 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 20 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | score                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | float                                                          |
+  | details | <ul><li>min: 14 tokens</li><li>mean: 25.5 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.1 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                                                                                            | sentence2                                                 | score            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------|:-----------------|
+  | <code>Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.</code> | <code>The woman is wearing white.</code>                  | <code>1.0</code> |
+  | <code>High fashion ladies wait outside a tram beside a crowd of people in the city.</code>                                           | <code>The women enjoy having a good fashion sense.</code> | <code>0.5</code> |
+  | <code>An older man is drinking orange juice at a restaurant.</code>                                                                  | <code>Two women are at a restaurant drinking wine.</code> | <code>0.0</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `num_train_epochs`: 1
+- `warmup_steps`: 0.05
+- `bf16`: True
+- `fp16_full_eval`: True
+- `load_best_model_at_end`: True
+- `push_to_hub`: True
+- `gradient_checkpointing`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `do_predict`: False
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 8
+- `per_device_eval_batch_size`: 8
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-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`: None
+- `warmup_ratio`: None
+- `warmup_steps`: 0.05
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `enable_jit_checkpoint`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `use_cpu`: False
+- `seed`: 42
+- `data_seed`: None
+- `bf16`: True
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: True
+- `tf32`: None
+- `local_rank`: -1
+- `ddp_backend`: None
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `push_to_hub`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: True
+- `gradient_checkpointing_kwargs`: None
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_num_input_tokens_seen`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `use_cache`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step   | Training Loss | Validation Loss | pair-score-evaluator-dev_spearman_cosine |
+|:-------:|:------:|:-------------:|:---------------:|:----------------------------------------:|
+| 0.1     | 1      | 2.7118        | -               | -                                        |
+| 0.5     | 5      | 4.8898        | -               | -                                        |
+| **1.0** | **10** | **3.096**     | **2.7557**      | **-0.2163**                              |
+| 0.1     | 1      | 2.4172        | -               | -                                        |
+| 0.5     | 5      | 2.9285        | -               | -                                        |
+| **1.0** | **10** | **2.8121**    | **3.2682**      | **-0.4146**                              |
+
+* The bold row denotes the saved checkpoint.
+
+### Training Time
+- **Training**: 3.7 minutes
+
+### Framework Versions
+- Python: 3.12.13
+- Sentence Transformers: 5.4.1
+- Transformers: 5.0.0
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.8.5
+- Tokenizers: 0.22.2
+
+## 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@article{10531646,
+    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
+    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
+    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
+    year={2024},
+    doi={10.1109/TASLP.2024.3402087}
+}
+```
+
+<!--
+## 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_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.10.0+cu128",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.0.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}