Training in progress, epoch 4, checkpoint

checkpoint-11038/1_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-11038/README.md

@@ -0,0 +1,426 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:705905
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: gerber baby food fruits apples bananas & cereal
+  sentences:
+  - world of sweets puzzle
+  - baby food
+  - baby food
+- source_sentence: granville original one bite original rice crispy squares
+  sentences:
+  - ' one bite rice crispy '
+  - sweet
+  - bounty wafer rolls
+- source_sentence: rosa / porcelain us andalusia mug
+  sentences:
+  - mug
+  - ' rosa mug'
+  - melamine small plate - teal
+- source_sentence: cetaphil sunscreen spf 50+ cream 89 ml
+  sentences:
+  - sunscreen
+  - ' cetaphil sunscreen cream'
+  - garnier intensity (6.60) intense ruby
+- source_sentence: italian dolce provolone
+  sentences:
+  - trident - gum strawberry flavor - 5 per pack
+  - experience the authentic taste of italy with our italian dolce provolone. indulge
+    in its creamy texture, delicate flavors, and versatility in both simple and sophisticated
+    culinary creations.
+  - dairy
+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: 0.9683457612991333
+      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/v3MiniLM-V18Data-256ConstantBATCH-SemanticEngine")
+# Run inference
+sentences = [
+    'italian dolce provolone',
+    'experience the authentic taste of italy with our italian dolce provolone. indulge in its creamy texture, delicate flavors, and versatility in both simple and sophisticated culinary creations.',
+    'trident - gum strawberry flavor - 5 per pack',
+]
+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, 0.8433, 0.2086],
+#         [0.8433, 1.0000, 0.2397],
+#         [0.2086, 0.2397, 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** | **0.9683** |
+
+<!--
+## 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: 705,905 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: 13.19 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.46 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.91 tokens</li><li>max: 11 tokens</li></ul> |
+* Samples:
+  | anchor                                         | positive                                 | itemCategory                   |
+  |:-----------------------------------------------|:-----------------------------------------|:-------------------------------|
+  | <code>mango nos  nos small</code>              | <code>milk chocolate ganache cake</code> | <code>sweet</code>             |
+  | <code>lux soap creamy perfection 165 gm</code> | <code>soap</code>                        | <code>hand soap</code>         |
+  | <code>grey deo original</code>                 | <code>classic deodrant</code>            | <code>women's deodorant</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: 2 tokens</li><li>mean: 6.53 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.52 tokens</li><li>max: 50 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>office supplies</code>      | <code>scary halloween skull mask</code>                                                        | <code>pencil</code>                 |
+  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code>superior </code>            | <code>coloring and writing book 21 x 29.7 cm 100 gsm 18 pages number subtraction ma4014</code> | <code>marker</code>                 |
+  | <code>first person singular author: haruki murakami</code>           | <code>haruki murakami book</code> | <code>buried secrets</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`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 6
+- `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/v3MiniLM-V18Data-256ConstantBATCH-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`: 2e-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`: 6
+- `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/v3MiniLM-V18Data-256ConstantBATCH-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 |
+|:------:|:-----:|:-------------:|:---------------:|:---------------:|
+| 0.0004 | 1     | 4.1707        | -               | -               |
+| 0.3626 | 1000  | 3.5534        | 0.5626          | 0.9461          |
+| 0.7252 | 2000  | 2.3098        | 0.4896          | 0.9515          |
+| 1.0877 | 3000  | 1.7306        | 0.4473          | 0.9593          |
+| 1.45   | 4000  | 1.8694        | 0.4308          | 0.9606          |
+| 1.8123 | 5000  | 1.6628        | 0.4218          | 0.9643          |
+| 2.1746 | 6000  | 1.5159        | 0.4153          | 0.9648          |
+| 2.5370 | 7000  | 1.435         | 0.4096          | 0.9669          |
+| 2.8993 | 8000  | 1.3973        | 0.3964          | 0.9683          |
+| 3.2616 | 9000  | 1.3101        | 0.3983          | 0.9674          |
+| 3.6239 | 10000 | 1.3044        | 0.3955          | 0.9680          |
+| 3.9862 | 11000 | 1.2367        | 0.3905          | 0.9683          |
+
+
+### 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-11038/config.json

@@ -0,0 +1,25 @@
+{
+  "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-11038/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__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"
+}

checkpoint-11038/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0193875ab6923a1c7996b650d335306aa397d545c5cd72e6bed1237414e5afd
+size 90864192

checkpoint-11038/modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

checkpoint-11038/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e68d8bf2baa0c8771305633a89d463de8c9e6faf9832e261cd6b430593f358e
+size 180607738

checkpoint-11038/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea8a901030bcf392d3335cbdc4e11b646241dd3f78834865ae783baf86be915b
+size 14244

checkpoint-11038/scaler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6024d721e62e8990ebe0b1c3504c56f6eb6824a55278955665e76e49893ff687
+size 988

checkpoint-11038/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90d475f0e557f43d7c8c950d00e0084ff6123e859d6c2af3e4b1240292e463fd
+size 1064

checkpoint-11038/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

checkpoint-11038/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "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": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-11038/tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

checkpoint-11038/trainer_state.json

@@ -0,0 +1,217 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 4.0,
+  "eval_steps": 1000,
+  "global_step": 11038,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.00036258158085569254,
+      "grad_norm": 7.228061676025391,
+      "learning_rate": 0.0,
+      "loss": 4.1707,
+      "step": 1
+    },
+    {
+      "epoch": 0.36258158085569253,
+      "grad_norm": 5.671091556549072,
+      "learning_rate": 6.036253776435046e-06,
+      "loss": 3.5534,
+      "step": 1000
+    },
+    {
+      "epoch": 0.36258158085569253,
+      "eval_cosine_accuracy": 0.9460511207580566,
+      "eval_loss": 0.562572181224823,
+      "eval_runtime": 36.4701,
+      "eval_samples_per_second": 260.734,
+      "eval_steps_per_second": 1.042,
+      "step": 1000
+    },
+    {
+      "epoch": 0.7251631617113851,
+      "grad_norm": 7.58541202545166,
+      "learning_rate": 1.20785498489426e-05,
+      "loss": 2.3098,
+      "step": 2000
+    },
+    {
+      "epoch": 0.7251631617113851,
+      "eval_cosine_accuracy": 0.9515196084976196,
+      "eval_loss": 0.48958125710487366,
+      "eval_runtime": 34.58,
+      "eval_samples_per_second": 274.986,
+      "eval_steps_per_second": 1.099,
+      "step": 2000
+    },
+    {
+      "epoch": 1.0876811594202898,
+      "grad_norm": 7.139122486114502,
+      "learning_rate": 1.8120845921450153e-05,
+      "loss": 1.7306,
+      "step": 3000
+    },
+    {
+      "epoch": 1.0876811594202898,
+      "eval_cosine_accuracy": 0.9593017101287842,
+      "eval_loss": 0.4472808539867401,
+      "eval_runtime": 34.9782,
+      "eval_samples_per_second": 271.855,
+      "eval_steps_per_second": 1.086,
+      "step": 3000
+    },
+    {
+      "epoch": 1.45,
+      "grad_norm": 6.1509857177734375,
+      "learning_rate": 1.8959057259404747e-05,
+      "loss": 1.8694,
+      "step": 4000
+    },
+    {
+      "epoch": 1.45,
+      "eval_cosine_accuracy": 0.9605636596679688,
+      "eval_loss": 0.4307531416416168,
+      "eval_runtime": 34.7892,
+      "eval_samples_per_second": 273.332,
+      "eval_steps_per_second": 1.092,
+      "step": 4000
+    },
+    {
+      "epoch": 1.8123188405797102,
+      "grad_norm": 5.870842933654785,
+      "learning_rate": 1.7449765825653423e-05,
+      "loss": 1.6628,
+      "step": 5000
+    },
+    {
+      "epoch": 1.8123188405797102,
+      "eval_cosine_accuracy": 0.9643495678901672,
+      "eval_loss": 0.4217974841594696,
+      "eval_runtime": 34.6903,
+      "eval_samples_per_second": 274.111,
+      "eval_steps_per_second": 1.095,
+      "step": 5000
+    },
+    {
+      "epoch": 2.17463768115942,
+      "grad_norm": 5.058931827545166,
+      "learning_rate": 1.5938963589666113e-05,
+      "loss": 1.5159,
+      "step": 6000
+    },
+    {
+      "epoch": 2.17463768115942,
+      "eval_cosine_accuracy": 0.9647701978683472,
+      "eval_loss": 0.4152602255344391,
+      "eval_runtime": 36.1266,
+      "eval_samples_per_second": 263.213,
+      "eval_steps_per_second": 1.052,
+      "step": 6000
+    },
+    {
+      "epoch": 2.5369565217391306,
+      "grad_norm": 4.676874160766602,
+      "learning_rate": 1.4429672155914791e-05,
+      "loss": 1.435,
+      "step": 7000
+    },
+    {
+      "epoch": 2.5369565217391306,
+      "eval_cosine_accuracy": 0.9668734669685364,
+      "eval_loss": 0.4095751941204071,
+      "eval_runtime": 34.6705,
+      "eval_samples_per_second": 274.268,
+      "eval_steps_per_second": 1.096,
+      "step": 7000
+    },
+    {
+      "epoch": 2.8992753623188405,
+      "grad_norm": 4.9811553955078125,
+      "learning_rate": 1.2918869919927484e-05,
+      "loss": 1.3973,
+      "step": 8000
+    },
+    {
+      "epoch": 2.8992753623188405,
+      "eval_cosine_accuracy": 0.9683457612991333,
+      "eval_loss": 0.3964327871799469,
+      "eval_runtime": 35.1522,
+      "eval_samples_per_second": 270.509,
+      "eval_steps_per_second": 1.081,
+      "step": 8000
+    },
+    {
+      "epoch": 3.261594202898551,
+      "grad_norm": 6.33431339263916,
+      "learning_rate": 1.1409578486176161e-05,
+      "loss": 1.3101,
+      "step": 9000
+    },
+    {
+      "epoch": 3.261594202898551,
+      "eval_cosine_accuracy": 0.9673992991447449,
+      "eval_loss": 0.39831292629241943,
+      "eval_runtime": 35.4714,
+      "eval_samples_per_second": 268.075,
+      "eval_steps_per_second": 1.071,
+      "step": 9000
+    },
+    {
+      "epoch": 3.623913043478261,
+      "grad_norm": 7.3262763023376465,
+      "learning_rate": 9.89877625018885e-06,
+      "loss": 1.3044,
+      "step": 10000
+    },
+    {
+      "epoch": 3.623913043478261,
+      "eval_cosine_accuracy": 0.9680302739143372,
+      "eval_loss": 0.3955402970314026,
+      "eval_runtime": 35.2264,
+      "eval_samples_per_second": 269.94,
+      "eval_steps_per_second": 1.079,
+      "step": 10000
+    },
+    {
+      "epoch": 3.986231884057971,
+      "grad_norm": 5.835394382476807,
+      "learning_rate": 8.390995618673516e-06,
+      "loss": 1.2367,
+      "step": 11000
+    },
+    {
+      "epoch": 3.986231884057971,
+      "eval_cosine_accuracy": 0.9683457612991333,
+      "eval_loss": 0.3905192017555237,
+      "eval_runtime": 35.7011,
+      "eval_samples_per_second": 266.351,
+      "eval_steps_per_second": 1.064,
+      "step": 11000
+    }
+  ],
+  "logging_steps": 1000,
+  "max_steps": 16548,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 6,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 256,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-11038/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b35c34ed649a8418b615ff1660402e4e4803bdecd02cad49b1ed62a5d928024e
+size 5752