Training in progress, epoch 2, checkpoint

checkpoint-6246/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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-6246/README.md

@@ -0,0 +1,423 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:799002
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: pappardelle beef ragu
+  sentences:
+  - chocolate molten
+  - slow-cooked pasta
+  - pasta
+  - pasta
+- source_sentence: cashmere pink braided knit top
+  sentences:
+  - backpack must 584612 monochrome black camo 32 x 19 x 42 cm greek
+  - casual wear
+  - top
+  - pink top
+- source_sentence: non vegan shish tawook
+  sentences:
+  - meat and poultry
+  - grilled vegetables shish tawook
+  - solid pink
+  - Restaurants & Cuisines
+- source_sentence: glysolid shower & care milk & honey 300 ml
+  sentences:
+  - growth hair oil
+  - ' shower gel'
+  - Skincare
+  - shower gel
+- source_sentence: triangle premium modern rug design
+  sentences:
+  - premium modern rug
+  - behind the lights
+  - Furniture
+  - furniture accessory
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9772945046424866
+      name: Cosine Accuracy
+---
+
+# 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 c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **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/huggingface/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, 'architecture': '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("LamaDiab/MiniLM-v2-v29-SemanticEngine")
+# Run inference
+sentences = [
+    'triangle premium modern rug design',
+    'premium modern rug',
+    'behind the lights',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8321, 0.1951],
+#         [0.8321, 1.0000, 0.2235],
+#         [0.1951, 0.2235, 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
+
+#### Triplet
+
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.9773** |
+
+<!--
+## 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: 799,002 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>itemCategory</code>, and <code>shoppingSubCategory_normalized</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | itemCategory                                                                    | shoppingSubCategory_normalized                                                 |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                          | string                                                                         |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.08 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.91 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.9 tokens</li><li>max: 7 tokens</li></ul> |
+* Samples:
+  | anchor                                          | positive                                        | itemCategory                  | shoppingSubCategory_normalized      |
+  |:------------------------------------------------|:------------------------------------------------|:------------------------------|:------------------------------------|
+  | <code>nivea cream 150 ml</code>                 | <code>body cream</code>                         | <code>body moisturizer</code> | <code>Skincare</code>               |
+  | <code>randel waxed canvas backpack – tan</code> | <code>padded laptop compartment backpack</code> | <code>bag</code>              | <code>Accessories</code>            |
+  | <code>stuffed chicken toast</code>              | <code>chicken</code>                            | <code>meat and poultry</code> | <code>Restaurants & Cuisines</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 9,381 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>itemCategory</code>, and <code>shoppingSubCategory_normalized</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          | negative                                                                         | itemCategory                                                                    | shoppingSubCategory_normalized                                                  |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | string                                                                           | string                                                                          | string                                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.55 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.31 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.02 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.78 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.82 tokens</li><li>max: 6 tokens</li></ul> |
+* Samples:
+  | anchor                                                               | positive                                   | negative                                  | itemCategory                        | shoppingSubCategory_normalized |
+  |:---------------------------------------------------------------------|:-------------------------------------------|:------------------------------------------|:------------------------------------|:-------------------------------|
+  | <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code>          | <code>office supplies</code>               | <code>lilac clouds kids prayer mat</code> | <code>pencil</code>                 | <code>Office Supplies</code>   |
+  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code>superior drawing marker</code>       | <code>luminous horror mask</code>         | <code>marker</code>                 | <code>Office Supplies</code>   |
+  | <code>first person singular author: haruki murakami</code>           | <code>penguin random house usa book</code> | <code>west el balad tablecloth</code>     | <code>literature and fiction</code> | <code>Books</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 3e-05
+- `weight_decay`: 0.01
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `dataloader_num_workers`: 1
+- `dataloader_prefetch_factor`: 2
+- `dataloader_persistent_workers`: True
+- `push_to_hub`: True
+- `hub_model_id`: LamaDiab/MiniLM-v2-v29-SemanticEngine
+- `hub_strategy`: all_checkpoints
+
+#### 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`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 0.01
+- `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`: 1
+- `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`: True
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: LamaDiab/MiniLM-v2-v29-SemanticEngine
+- `hub_strategy`: all_checkpoints
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `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
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: 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 | cosine_accuracy |
+|:------:|:----:|:-------------:|:---------------:|:---------------:|
+| 0.0003 | 1    | 2.2696        | -               | -               |
+| 0.3203 | 1000 | 1.6542        | 0.5916          | 0.9642          |
+| 0.6406 | 2000 | 1.0828        | 0.5482          | 0.9689          |
+| 0.9609 | 3000 | 0.8294        | 0.5289          | 0.9719          |
+| 1.2810 | 4000 | 0.762         | 0.5255          | 0.9752          |
+| 1.6012 | 5000 | 0.7273        | 0.5073          | 0.9756          |
+| 1.9213 | 6000 | 0.6962        | 0.4961          | 0.9773          |
+
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.1.2
+- Transformers: 4.53.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.9.0
+- Datasets: 4.4.1
+- Tokenizers: 0.21.2
+
+## 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",
+}
+```
+
+<!--
+## 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-6246/config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
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+  "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": "4.53.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-6246/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.53.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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checkpoint-6246/modules.json

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+    "type": "sentence_transformers.models.Pooling"
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+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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checkpoint-6246/sentence_bert_config.json

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

checkpoint-6246/special_tokens_map.json

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+  }
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checkpoint-6246/tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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checkpoint-6246/trainer_state.json

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