Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:101
+- loss:MultipleNegativesRankingLoss
+base_model: nomic-ai/modernbert-embed-base
+widget:
+- source_sentence: 'Question: How long do I have to complete the biometric verification?  Answer:
+    Once you receive the OTP, you must finish the biometric process within 45 days.
+    An automated email will remind you to complete it.'
+  sentences:
+  - What's the deadline for that fingerprint thing after I get the code?
+  - I'm disputing my capital gains tax calculation from NCCPL, what can I do?
+  - 'Question: Can I complete the biometric verification from outside Pakistan?  Answer:
+    Yes, if you are currently abroad, you can complete the biometric process either
+    online or by visiting an NCCPL office when you return to Pakistan, provided its
+    within 45 days of account activation.'
+- source_sentence: 'Question: What happens if I don''t pay CGT on my PSX transactions?  Answer:
+    If you don''t pay CGT on your PSX transactions, the National Clearing Company
+    of Pakistan Limited (NCCPL) can block your account. This means you won''t be able
+    to trade or access your holdings until the outstanding CGT is paid. It is important
+    to ensure that all CGT obligations are met to avoid any disruptions in your trading
+    account.'
+  sentences:
+  - 'Question: How can I make zakat non-deductible?  Answer: To make zakat non-deductible,
+    you need to submit a declaration on stamp paper as per regulatory requirements
+    of NCCPL. We can prepare the paperwork for you; however, you will need to sign
+    it and pay an additional fee of PKR 500/- for stamp paper. If you wish, you can
+    initially set zakat as deductible and change it later.'
+  - My app keeps crashing!  What should I do?
+  - What if I forget to pay taxes on my PSX trades?
+- source_sentence: 'Question: What should I do if my IBAN is not verified by RAAST?  Answer:
+    Your IBAN might be incorrect, please verify it and share the correct one with
+    us. The error is due to a mismatch between your bank details and Finqalab account
+    details, please try using a different bank account or a mobile wallet (Easypaisa/JazzCash)
+    that is under your name.  If you do not have another bank account, please send
+    a copy of your cheque or account maintenance certificate for processing. If you
+    provided an RDA account, please try using a local mobile wallet like Easypaisa
+    or JazzCash under your name instead.'
+  sentences:
+  - My IBAN isn't working with RAAST, why is this?
+  - 'Question: How to pay through bank transfers?  Answer: To pay through a bank transfer
+    to our MCB Account, go to the account screen on the app, select My Payment Deposit'',
+    select Bank Transfer and then make a bank transfer on the given IBAN number. Once
+    the transaction has been made, enter your account details and lastly, upload the
+    receipt of deposit as proof. It takes 24 hours to process the payment. We only
+    recommend this for transactions above PKR 1 million. Bank Transfer Initiate a
+    Bank Transfer MCB Bank Next Capital Limited PK62MUCB0550019331001281 0550 0193
+    3100 1281 Enter Details'
+  - Will my phone run this app? I'm worried about the specs.
+- source_sentence: 'Question: Are bonus shares taxable?  Answer: In Pakistan, bonus
+    shares are subject to tax at the rate of 10%.'
+  sentences:
+  - I'm wondering if I have to pay tax on bonus shares I received?
+  - 'Question: Can I sell my bonus shares?  Answer: Yes, once you receive the bonus
+    shares, they become regular shares, and you can sell them on the stock exchange,
+    just like any other shares you own.'
+  - When should I expect the money in my account?
+- source_sentence: 'Question: I''m experiencing issues with logging in to the app.
+    What should I do?  Answer: In case you are facing any issues, Try closing the
+    app and opening it again. Try clearing the cache or updating to the latest version.
+    If the issue still persists, contact our customer support through Whatsapp (+923003672522).
+    Email your query at support@finqalab.com.pk'
+  sentences:
+  - 'Question: The app is not loading properly on my device. What could be the problem?  Answer:
+    If the app isn''t loading properly: Please check if you have a stable internet
+    connection. Try refreshing the screen 2-3 times. Close the app and open it again.
+    Try clearing the cache, check for app updates or reinstall the app. If the issue
+    still persists, contact our customer support through Whatsapp (+923003672522)
+    or email your query at support@finqalab.com'
+  - Why isn't my text going through to 9646?
+  - My app won't let me log in!  Help!
+datasets:
+- Ch333tah/finqalab_embedding_finetune
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on nomic-ai/modernbert-embed-base
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: ai job train
+      type: ai-job-train
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9603960514068604
+      name: Cosine Accuracy
+    - type: cosine_accuracy
+      value: 0.9603960514068604
+      name: Cosine Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: ai job valid
+      type: ai-job-valid
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9047619104385376
+      name: Cosine Accuracy
+    - type: cosine_accuracy
+      value: 0.9523809552192688
+      name: Cosine Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: ai job test
+      type: ai-job-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9130434989929199
+      name: Cosine Accuracy
+    - type: cosine_accuracy
+      value: 0.9130434989929199
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on nomic-ai/modernbert-embed-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the [finqalab_embedding_finetune](https://huggingface.co/datasets/Ch333tah/finqalab_embedding_finetune) dataset. It maps sentences & paragraphs to a 768-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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [finqalab_embedding_finetune](https://huggingface.co/datasets/Ch333tah/finqalab_embedding_finetune)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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("Ch333tah/modernbert-finqalab-embeddings")
+# Run inference
+sentences = [
+    "Question: I'm experiencing issues with logging in to the app. What should I do?  Answer: In case you are facing any issues, Try closing the app and opening it again. Try clearing the cache or updating to the latest version. If the issue still persists, contact our customer support through Whatsapp (+923003672522). Email your query at support@finqalab.com.pk",
+    "My app won't let me log in!  Help!",
+    "Question: The app is not loading properly on my device. What could be the problem?  Answer: If the app isn't loading properly: Please check if you have a stable internet connection. Try refreshing the screen 2-3 times. Close the app and open it again. Try clearing the cache, check for app updates or reinstall the app. If the issue still persists, contact our customer support through Whatsapp (+923003672522) or email your query at support@finqalab.com",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+
+* Datasets: `ai-job-train`, `ai-job-valid`, `ai-job-test`, `ai-job-train`, `ai-job-valid` and `ai-job-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | ai-job-train | ai-job-valid | ai-job-test |
+|:--------------------|:-------------|:-------------|:------------|
+| **cosine_accuracy** | **0.9604**   | **0.9524**   | **0.913**   |
+
+<!--
+## 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
+
+#### finqalab_embedding_finetune
+
+* Dataset: [finqalab_embedding_finetune](https://huggingface.co/datasets/Ch333tah/finqalab_embedding_finetune) at [144dee2](https://huggingface.co/datasets/Ch333tah/finqalab_embedding_finetune/tree/144dee2d0b0590067701cd658ac405ccd702e731)
+* Size: 101 training samples
+* Columns: <code>Pos_Context</code>, <code>Query</code>, and <code>Neg_Context</code>
+* Approximate statistics based on the first 101 samples:
+  |         | Pos_Context                                                                        | Query                                                                             | Neg_Context                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 27 tokens</li><li>mean: 74.1 tokens</li><li>max: 418 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.19 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 72.42 tokens</li><li>max: 231 tokens</li></ul> |
+* Samples:
+  | Pos_Context                                                                                                                                                                                                                                                                            | Query                                                                             | Neg_Context                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Question: I did not receive a verification email? What should I do?  Answer: Please check your spam folder and double check your registered email address. If you still dont see a verification email, contact our customer support department at support@finqalab.com.</code>   | <code>My verification email didn't arrive, any ideas?</code>                      | <code>Question: I did not receive my instant transfer in Finqalab account within 10 minutes. What should I do?  Answer: If your instant transfer hasnt been credited to your Finqalab account within 10 minutes, please email us at support@finqalab.com with a screenshot of the receipt or send it to us on Whatsapp (+923003672522). Our team will review the issue, escalate it to the bank by sending the transaction receipt, and follow up to ensure your funds are credited promptly.</code> |
+  | <code>Question: What are the applicable CGT rates for RDA Account Holders?  Answer: Filer rates are applied to RDA account holders irrespective of their status (Filer or Non-filer).</code>                                                                                           | <code>How are capital gains taxes handled for someone with an RDA account?</code> | <code>Question: What does Minimum Lot Size mean?  Answer: This means that you need to buy a minimum quantity for a share. In case of ETFs the minimum lot size is 500 or in multiples of 500 shares. Whereas, for non-ETF stocks the minimum lot size is 1 share.</code>                                                                                                                                                                                                                             |
+  | <code>Question: How do I receive bonus shares?  Answer: Bonus shares are distributed to shareholders based on a ratio announced by the company. For example, if a company declares a 20% bonus issue, you will receive 2 additional shares for every 10 shares you already own.</code> | <code>What's the deal with getting extra shares?</code>                           | <code>Question: Do I have to pay for bonus shares?  Answer: No, bonus shares are issued free of charge. They are typically paid for by utilizing the companys retained earnings or reserves.</code>                                                                                                                                                                                                                                                                                                  |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### finqalab_embedding_finetune
+
+* Dataset: [finqalab_embedding_finetune](https://huggingface.co/datasets/Ch333tah/finqalab_embedding_finetune) at [144dee2](https://huggingface.co/datasets/Ch333tah/finqalab_embedding_finetune/tree/144dee2d0b0590067701cd658ac405ccd702e731)
+* Size: 21 evaluation samples
+* Columns: <code>Pos_Context</code>, <code>Query</code>, and <code>Neg_Context</code>
+* Approximate statistics based on the first 21 samples:
+  |         | Pos_Context                                                                         | Query                                                                              | Neg_Context                                                                        |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 23 tokens</li><li>mean: 78.67 tokens</li><li>max: 152 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 16.67 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 91.9 tokens</li><li>max: 418 tokens</li></ul> |
+* Samples:
+  | Pos_Context                                                                                                                                                                                                                                                                                                                                                       | Query                                                                             | Neg_Context                                                                                                                                                                                                                                                                                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Question: How will I know if my biometric verification is pending or close to the deadline?  Answer: We receive reports every Monday and Friday regarding users with pending biometric verifications. If you have less than 7 days remaining, we will contact you to remind you to complete the process or provide alternate solutions if necessary.</code> | <code>What happens if my biometric verification is about to expire?</code>        | <code>Question: I entered my CNIC in the Bioverify app and got CNIC not eligible for this service message. What does this mean?  Answer: This means that the Biometric verification is not required.</code>                                                                                            |
+  | <code>Question: How long do I have to complete the biometric verification?  Answer: Once you receive the OTP, you must finish the biometric process within 45 days. An automated email will remind you to complete it.</code>                                                                                                                                     | <code>What's the deadline for that fingerprint thing after I get the code?</code> | <code>Question: Can I complete the biometric verification from outside Pakistan?  Answer: Yes, if you are currently abroad, you can complete the biometric process either online or by visiting an NCCPL office when you return to Pakistan, provided its within 45 days of account activation.</code> |
+  | <code>Question: Is historical price data available for stocks in the app?  Answer: Yes, it is.</code>                                                                                                                                                                                                                                                             | <code>Can I see stock prices from the past in this app?</code>                    | <code>Question: How often is the stock data updated in the app?  Answer: The stock data is updated in real time.</code>                                                                                                                                                                                |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### 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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 0.0
+- `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`: {}
+- `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`: False
+- `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`: 0
+- `dataloader_prefetch_factor`: None
+- `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`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | ai-job-train_cosine_accuracy | ai-job-valid_cosine_accuracy | ai-job-test_cosine_accuracy |
+|:-----:|:----:|:----------------------------:|:----------------------------:|:---------------------------:|
+| -1    | -1   | 0.9604                       | 0.9524                       | 0.9130                      |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.0
+- Transformers: 4.48.1
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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.json

@@ -0,0 +1,47 @@
+{
+  "_name_or_path": "nomic-ai/modernbert-embed-base",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
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