Training in progress, epoch 5, checkpoint

checkpoint-27515/1_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-27515/README.md

@@ -0,0 +1,439 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:704308
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: must kindergarten backpack mermazing 2 cases
+  sentences:
+  - wide leg popline pants b22
+  - ' kindergarten mermazing backpack '
+  - bag
+- source_sentence: derby cap toe shoes - brown
+  sentences:
+  - natural leather shoes
+  - shoe
+  - 925 sterling silver heart ear studs with  genuine european crystals
+- source_sentence: rembrandt's eyes
+  sentences:
+  - art book
+  - ' rembrandt''s eyes book'
+  - canvas frame 100% cotton 350 gsm 20 cm triangle m e5303t
+- source_sentence: essence multi task concealer 15 natural nude
+  sentences:
+  - face make-up
+  - ' essence concealer'
+  - rowntrees fruit pastilles
+- source_sentence: parker ingenuity ct black lacquer so959210
+  sentences:
+  - lagu-family barber shop toy
+  - ' pen'
+  - pen
+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: 0.9601430296897888
+      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/NewMiniLM-V15Data-128BATCH-SemanticEngine")
+# Run inference
+sentences = [
+    'parker ingenuity ct black lacquer so959210',
+    ' pen',
+    'lagu-family barber shop toy',
+]
+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,  0.2524, -0.0132],
+#         [ 0.2524,  1.0000,  0.1220],
+#         [-0.0132,  0.1220,  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** | **0.9601** |
+
+<!--
+## 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: 704,308 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>itemCategory</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         | itemCategory                                                                    |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | string                                                                          |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 8.06 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.35 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.93 tokens</li><li>max: 9 tokens</li></ul> |
+* Samples:
+  | anchor                                                       | positive                                          | itemCategory                         |
+  |:-------------------------------------------------------------|:--------------------------------------------------|:-------------------------------------|
+  | <code>rilastil sunlaude comfort dye fluid spf50 50 ml</code> | <code>spf50 sunscreen</code>                      | <code>sunscreen</code>               |
+  | <code>lemon and powder leather slippers</code>               | <code>genuine cow leather</code>                  | <code>slipper</code>                 |
+  | <code>erastapex  trio</code>                                 | <code>erastapex  trio olmesartan medoxomil</code> | <code>blood disorder medicine</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: 9,509 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, and <code>itemCategory</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          | negative                                                                         | itemCategory                                                                     |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.63 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.17 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.79 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 10 tokens</li></ul> |
+* Samples:
+  | anchor                                                               | positive                          | negative                                                  | itemCategory                        |
+  |:---------------------------------------------------------------------|:----------------------------------|:----------------------------------------------------------|:------------------------------------|
+  | <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code>          | <code>0.7 mm pencil</code>        | <code>tracing sketch a3 70 gr 50 sheets</code>            | <code>pencil</code>                 |
+  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code> marker pen set </code>     | <code>wunder chocolate strawberry ganache & coulis</code> | <code>marker</code>                 |
+  | <code>first person singular author: haruki murakami</code>           | <code>haruki murakami book</code> | <code>dark hot chocolate sugar free</code>                | <code>literature and fiction</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`: 128
+- `per_device_eval_batch_size`: 128
+- `weight_decay`: 0.001
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.2
+- `fp16`: True
+- `dataloader_num_workers`: 1
+- `dataloader_prefetch_factor`: 2
+- `dataloader_persistent_workers`: True
+- `push_to_hub`: True
+- `hub_model_id`: LamaDiab/NewMiniLM-V15Data-128BATCH-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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 5e-05
+- `weight_decay`: 0.001
+- `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`: 0.2
+- `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/NewMiniLM-V15Data-128BATCH-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 |
+|:------:|:-----:|:-------------:|:---------------:|:---------------:|
+| 0.0002 | 1     | 3.1229        | -               | -               |
+| 0.1817 | 1000  | 2.6857        | 1.6310          | 0.9441          |
+| 0.3634 | 2000  | 2.0541        | 1.5448          | 0.9472          |
+| 0.5452 | 3000  | 1.7335        | 1.5236          | 0.9485          |
+| 0.7269 | 4000  | 1.2495        | 1.5552          | 0.9433          |
+| 0.9086 | 5000  | 0.813         | 1.5794          | 0.9472          |
+| 1.0903 | 6000  | 1.0512        | 1.4544          | 0.9567          |
+| 1.2720 | 7000  | 1.2912        | 1.4492          | 0.9563          |
+| 1.4538 | 8000  | 1.1994        | 1.4519          | 0.9568          |
+| 1.6355 | 9000  | 1.0662        | 1.4635          | 0.9545          |
+| 1.8172 | 10000 | 0.6724        | 1.5717          | 0.9454          |
+| 1.9989 | 11000 | 0.4761        | 1.5509          | 0.9503          |
+| 2.1806 | 12000 | 1.0468        | 1.4510          | 0.9591          |
+| 2.3623 | 13000 | 0.9871        | 1.4625          | 0.9608          |
+| 2.5441 | 14000 | 0.9596        | 1.4531          | 0.9606          |
+| 2.7258 | 15000 | 0.7272        | 1.4685          | 0.9589          |
+| 2.9075 | 16000 | 0.4716        | 1.5063          | 0.9549          |
+| 3.0892 | 17000 | 0.6495        | 1.4401          | 0.9626          |
+| 3.2709 | 18000 | 0.8911        | 1.4418          | 0.9642          |
+| 3.4527 | 19000 | 0.871         | 1.4658          | 0.9635          |
+| 3.6344 | 20000 | 0.8008        | 1.4879          | 0.9594          |
+| 3.8161 | 21000 | 0.5084        | 1.4949          | 0.9579          |
+| 3.9978 | 22000 | 0.3552        | 1.5567          | 0.9568          |
+| 4.1795 | 23000 | 0.8254        | 1.4609          | 0.9651          |
+| 4.3613 | 24000 | 0.8164        | 1.4704          | 0.9641          |
+| 4.5430 | 25000 | 0.8078        | 1.4598          | 0.9635          |
+| 4.7247 | 26000 | 0.6181        | 1.4891          | 0.9602          |
+| 4.9064 | 27000 | 0.3932        | 1.4990          | 0.9601          |
+
+
+### 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-27515/config.json

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+{
+  "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": "4.53.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-27515/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-27515/modules.json

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

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checkpoint-27515/sentence_bert_config.json

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

checkpoint-27515/special_tokens_map.json

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

checkpoint-27515/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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+      "special": true
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