changAI-nomic-embed-text-v1.5-finetuned

SentenceTransformer based on hyrinmansoor/changAI-nomic-embed-text-v1.5-finetuned

This is a sentence-transformers model finetuned from hyrinmansoor/changAI-nomic-embed-text-v1.5-finetuned. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: hyrinmansoor/changAI-nomic-embed-text-v1.5-finetuned <!-- at revision 02a5a785884c99ed26261240dc8cc79e1dc40f40 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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': 'ModernBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'revenue from warenty claim items vs non-claimed lst yr',
    '[FIELD] item_code | [TABLE] tabWarranty Claim | desc: links to the Item master; identifies the product subject to a warranty claim',
    'Support_027',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.6816, -0.0659],
#         [ 0.6816,  1.0000,  0.0028],
#         [-0.0659,  0.0028,  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.* --> <!-- ## 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: 262,905 training samples
• Columns: <code>anchor</code>, <code>positive</code>, <code>qid</code>, and <code>module</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	qid	module
  type	string	string	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 16.09 tokens</li><li>max: 45 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 28.94 tokens</li><li>max: 71 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 6.44 tokens</li><li>max: 8 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.97 tokens</li><li>max: 5 tokens</li></ul>

• Samples:

  anchor	positive	qid	module
  <code>how many days has the work order WO-999 been open?</code>	<code>[FIELD] name | [TABLE] tabWork Order | desc: Unique identifier for the production order.</code>	<code>Manufacturing_931</code>	<code>Manufacturing</code>
  <code>Whats the total amount invoiced for product XYZ?</code>	<code>[FIELD] item_code | [TABLE] tabInvoice Item | desc: Code of the item on the invoice line.</code>	<code>Selling_2982</code>	<code>Selling</code>
  <code>Which quality meetings are missing their corrective actions?</code>	<code>[FIELD] quality_meeting | [TABLE] tabQuality Action | desc: Links a quality action item back to the specific quality meeting where it was generated; if NULL for a meeting, indicates no actions were planned for it.</code>	<code>Quality Management_233</code>	<code>Quality Management</code>

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 4,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 13,837 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, <code>qid</code>, and <code>module</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	qid	module
  type	string	string	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 16.25 tokens</li><li>max: 58 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 29.28 tokens</li><li>max: 300 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 6.39 tokens</li><li>max: 8 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 3.97 tokens</li><li>max: 5 tokens</li></ul>

• Samples:

  anchor	positive	qid	module
  <code>Opportunities automatically closed based on CRM setting?</code>	<code>[FIELD] transaction_date | [TABLE] tabOpportunity | desc: the date the opportunity was recorded or transacted; compare with expected closing date and CRM setting</code>	<code>CRM_791</code>	<code>CRM</code>
  <code>purchase amount last year vs year before</code>	<code>[FIELD] purchase_date | [TABLE] tabAsset | desc: the date the asset was acquired; used to filter assets by acquisition year</code>	<code>Assets_123</code>	<code>Assets</code>
  <code>Count how many tickets were resolved by Sarah.</code>	<code>[TABLE] tabUser | desc: Master data for users.</code>	<code>Support_273</code>	<code>Support</code>

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 4,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 12
• per_device_eval_batch_size: 12
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_steps: 0.1
• fp16: True
• load_best_model_at_end: True

All Hyperparameters

<details><summary>Click to expand</summary>

• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 12
• per_device_eval_batch_size: 12
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-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: 0.1
• 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: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• 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: False
• 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: False
• 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

<details><summary>Click to expand</summary>

• The bold row denotes the saved checkpoint.

</details>

Training Time

• Training: 5.8 hours

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: 4.0.0
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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