Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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

@@ -0,0 +1,552 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:498970
+- loss:BPRLoss
+base_model: nomic-ai/nomic-embed-text-v2-moe
+widget:
+- source_sentence: what was the start treaty 2010
+  sentences:
+  - "Strategic Offensive Reductions: The Treaty between the United States of America\
+    \ and the Russian Federation on Measures for the Further Reduction and Limitation\
+    \ of Strategic Offensive Arms, also known as the New START Treaty, entered into\
+    \ force on February 5, 2011.nder the Treaty, the United States and Russia must\
+    \ meet the Treatyâ\x80\x99s central limits on strategic arms by February 5, 2018;\
+    \ seven years from the date the Treaty entered into force. Each Party has the\
+    \ flexibility to determine for itself the structure of its strategic forces within\
+    \ the aggregate limits of the Treaty."
+  - 'Nuclear pharmacy practice: hour-for-hour credit in a licensed nuclear pharmacy
+    or health care facility approved by state or federal agencies to handle radioactive
+    materials, to a maximum of 4,000 hours.'
+  - 'Signed: 18 June 1979. Entered into Force: Never entered into force; superseded
+    by the START I Treaty in 1991. Duration: Until 31 December 1985; unless the Treaty
+    is replaced earlier by an agreement further limiting strategic offensive arms.
+    Parties: Soviet Union and United States.'
+- source_sentence: is pez a word
+  sentences:
+  - From dispensers to candy, there's a PEZ for anyone and everyone. Look for these
+    PEZ products at your local retailer.rom dispensers to candy, there's a PEZ for
+    anyone and everyone. Look for these PEZ products at your local retailer.
+  - PEZ was first introduced in 1927 in Vienna, Austria as a breath mint for adults!
+    The word PEZ was created using the first, middle and last letter in the German
+    word for peppermint P feff E rmin Z.
+  - Boonville is a city in Boon Township, Warrick County, Indiana, United States.
+    The population was 6,246 at the 2010 census.The city is the county seat of Warrick
+    County.oonville was founded in 1818 and named for Jesse Boon, father of Ratliff
+    Boon. A post office has been in operation at Boonville since 1820. Boonville was
+    incorporated in 1858.
+- source_sentence: us budget deficit by president
+  sentences:
+  - "By 2022, the government will once again be running trillion-dollar deficits,\
+    \ the report said. â\x80\x9CWe still have a lot of work to do,â\x80\x9D said House\
+    \ Budget Committee Chairman Paul Ryan. Lawmakers can take some credit for the\
+    \ short-term improvement in the budget outlook, the report showed, though the\
+    \ strengthening economy helps as well."
+  - However, when they are 3 to 4 months old, they become susceptible to the disease,
+    so all calves should be vaccinated for blackleg by 4 months of age. A revaccination
+    3 to 6 weeks later according to product label directions is necessary to provide
+    the best protec-tion.lackleg seldom affects cattle older than 2 years of age,
+    most likely due to immunity induced by vaccines or natural exposure. However,
+    sporadic cases do occur in cattle older than 2 years and are often associated
+    with the reuse of needles for multiple injections.
+  - According to this method, Barack Obama's budget is projected to run a deficit
+    of $7.3 trillion over his eight years, making him the president with the largest
+    budget deficit. George W. Bush is second, with a deficit of $3.29 trillion over
+    his eight years.
+- source_sentence: what is a sixth sense
+  sentences:
+  - 1 Extrasensory perception (ESP), commonly called the sixth sense. 2  Equilibrioception
+    (sense of balance) and proprioception (sense of body position), commonly accepted
+    physiological senses in addition to the usually considered five senses.
+  - 'Glaze or glazing may refer to: 1  Glaze (metallurgy), a layer of compacted sintered
+    oxide formed on some metals. 2  Glaze (cooking technique), a coating of a glossy,
+    often sweet, mixture applied to food.  Glaze (painting technique), a layer of
+    paint, thinned with a medium, so as to become somewhat transparent.'
+  - Definition of Proprioception. The term proprioception is used to describe the
+    sensory information that contributes to the sense of position of self and movement.
+    Sir Charles Bell named the sixth sense as the sense of the positions and actions
+    of the limbs (McCloskey 1978).eceptors of Proprioception. It is well recognized
+    that joint movements activate receptors in the joint, skin and muscle. In turn,
+    any of these receptors may play a role in the perception and control of limb movement
+    and joint angle.
+- source_sentence: what services are offered by adult day care
+  sentences:
+  - The Met Life Market survey of 2008 on adult day services states the average cost
+    for adult day care services is $64 per day. There has been an increase of 5% in
+    these services in the past year.
+  - Consumer Guide to Long Term Care. Adult Day Care. Adult day care is a planned
+    program offered in a group setting that provides services that improve or maintain
+    health or functioning, and social activities for seniors and persons with disabilities.
+  - As nouns the difference between tackle and guard is that tackle is (nautical)
+    a system of ropes and blocks used to increase the force applied to the free end
+    of the rope while guard is a person who, or thing that, protects or watches over
+    something. As verbs the difference between tackle and guard
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on nomic-ai/nomic-embed-text-v2-moe
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe). 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/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) <!-- at revision f6a8873b415144a69ffc529ec1e234d1e00ee765 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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/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: NomicBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## 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("BlackBeenie/nomic-embed-text-v2-moe-msmarco-bpr")
+# Run inference
+sentences = [
+    'what services are offered by adult day care',
+    'Consumer Guide to Long Term Care. Adult Day Care. Adult day care is a planned program offered in a group setting that provides services that improve or maintain health or functioning, and social activities for seniors and persons with disabilities.',
+    'The Met Life Market survey of 2008 on adult day services states the average cost for adult day care services is $64 per day. There has been an increase of 5% in these services in the past year.',
+]
+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.*
+-->
+
+<!--
+## 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: 498,970 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                          | sentence_2                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.75 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 89.23 tokens</li><li>max: 241 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 86.66 tokens</li><li>max: 280 tokens</li></ul> |
+* Samples:
+  | sentence_0                                      | sentence_1                                                                                                                                                                                                                                                                                                                               | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what the history of bluetooth</code>      | <code>When asked about the name Bluetooth, I explained that Bluetooth was borrowed from the 10th century, second King of Denmark, King Harald Bluetooth; who was famous for uniting Scandinavia just as we intended to unite the PC and cellular industries with a short-range wireless link.</code>                                     | <code>Technology: 1  How secure is a Bluetooth network? 2  What is Frequency-Hopping Spread Spectrum (FHSS)? 3  Will other RF (Radio Frequency) devices interfere with Bluetooth Devices? 4  Will Bluetooth and Wireless LAN (WLAN) interfere with each other? 5  What is the data throughput speed of a Bluetooth connection? 6  What is the range of Bluetooth 7  ... What kind of ...</code>                                                                           |
+  | <code>how thin can a concrete slab be</code>    | <code>Another issue that must be addressed is the added weight of the thin-slab. Poured gypsum thin-slabs typically add 13 to 15 pounds per square foot to the dead loading of a floor structure. Standard weight concrete thin slabs add about 18 pounds per square foot (at 1.5 thickness).</code>                                     | <code>Find the Area in square feet: We will use a concrete slab pour for our example. Let’s say that we need to figure out the yardage for a slab that will be 15 feet long by 10 feet wide and 4 inches thick. First we find the area by multiplying the length times the width. 1  15 feet X 10 feet = 150 square feet.</code>                                                                                                                                        |
+  | <code>how long to cook eggs to hard boil</code> | <code>This method works best if the eggs are in a single layer, but you can double them up as well, you'll just need to add more time to the steaming time. 3 Set your timer for 6 minutes for soft boiled, 10 minutes for hard boiled with a still translucent and bright yolk, or 12-15 minutes for cooked-through hard boiled.</code> | <code>Hard-Steamed Eggs. Fill a pot that can comfortably hold your steamer with the lid on with 1 to 2 inches of water. Bring to a rolling boil, 212 degrees Fahrenheit. Place your eggs in a metal steamer, and lower the basket into the pot. The eggs should sit above the boiling water. Cover and cook for 12 minutes. Hard-steamed eggs, like hard-boiled eggs, are eggs that are cooked until the egg yolk is fully set and has turned to a chalky texture.</code> |
+* Loss: <code>beir.losses.bpr_loss.BPRLoss</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 5
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: 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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0321 | 500   | 0.3396        |
+| 0.0641 | 1000  | 0.2094        |
+| 0.0962 | 1500  | 0.21          |
+| 0.1283 | 2000  | 0.1955        |
+| 0.1603 | 2500  | 0.1989        |
+| 0.1924 | 3000  | 0.1851        |
+| 0.2245 | 3500  | 0.1839        |
+| 0.2565 | 4000  | 0.1859        |
+| 0.2886 | 4500  | 0.1892        |
+| 0.3207 | 5000  | 0.1865        |
+| 0.3527 | 5500  | 0.1773        |
+| 0.3848 | 6000  | 0.1796        |
+| 0.4169 | 6500  | 0.1929        |
+| 0.4489 | 7000  | 0.1829        |
+| 0.4810 | 7500  | 0.172         |
+| 0.5131 | 8000  | 0.1792        |
+| 0.5451 | 8500  | 0.1747        |
+| 0.5772 | 9000  | 0.1802        |
+| 0.6092 | 9500  | 0.1856        |
+| 0.6413 | 10000 | 0.1751        |
+| 0.6734 | 10500 | 0.173         |
+| 0.7054 | 11000 | 0.1774        |
+| 0.7375 | 11500 | 0.1722        |
+| 0.7696 | 12000 | 0.1825        |
+| 0.8016 | 12500 | 0.1714        |
+| 0.8337 | 13000 | 0.1732        |
+| 0.8658 | 13500 | 0.167         |
+| 0.8978 | 14000 | 0.1792        |
+| 0.9299 | 14500 | 0.1697        |
+| 0.9620 | 15000 | 0.1682        |
+| 0.9940 | 15500 | 0.1764        |
+| 1.0    | 15593 | -             |
+| 1.0261 | 16000 | 0.0875        |
+| 1.0582 | 16500 | 0.0798        |
+| 1.0902 | 17000 | 0.0764        |
+| 1.1223 | 17500 | 0.0783        |
+| 1.1544 | 18000 | 0.0759        |
+| 1.1864 | 18500 | 0.0834        |
+| 1.2185 | 19000 | 0.082         |
+| 1.2506 | 19500 | 0.0827        |
+| 1.2826 | 20000 | 0.0876        |
+| 1.3147 | 20500 | 0.0819        |
+| 1.3468 | 21000 | 0.0841        |
+| 1.3788 | 21500 | 0.0815        |
+| 1.4109 | 22000 | 0.0819        |
+| 1.4430 | 22500 | 0.0883        |
+| 1.4750 | 23000 | 0.0826        |
+| 1.5071 | 23500 | 0.0837        |
+| 1.5392 | 24000 | 0.086         |
+| 1.5712 | 24500 | 0.0806        |
+| 1.6033 | 25000 | 0.0918        |
+| 1.6353 | 25500 | 0.0885        |
+| 1.6674 | 26000 | 0.0885        |
+| 1.6995 | 26500 | 0.088         |
+| 1.7315 | 27000 | 0.0843        |
+| 1.7636 | 27500 | 0.0915        |
+| 1.7957 | 28000 | 0.0843        |
+| 1.8277 | 28500 | 0.0868        |
+| 1.8598 | 29000 | 0.0857        |
+| 1.8919 | 29500 | 0.0931        |
+| 1.9239 | 30000 | 0.0852        |
+| 1.9560 | 30500 | 0.0913        |
+| 1.9881 | 31000 | 0.0857        |
+| 2.0    | 31186 | -             |
+| 2.0201 | 31500 | 0.0547        |
+| 2.0522 | 32000 | 0.0459        |
+| 2.0843 | 32500 | 0.0451        |
+| 2.1163 | 33000 | 0.0407        |
+| 2.1484 | 33500 | 0.0469        |
+| 2.1805 | 34000 | 0.0459        |
+| 2.2125 | 34500 | 0.0508        |
+| 2.2446 | 35000 | 0.0508        |
+| 2.2767 | 35500 | 0.0518        |
+| 2.3087 | 36000 | 0.0552        |
+| 2.3408 | 36500 | 0.0491        |
+| 2.3729 | 37000 | 0.0575        |
+| 2.4049 | 37500 | 0.0558        |
+| 2.4370 | 38000 | 0.0475        |
+| 2.4691 | 38500 | 0.0486        |
+| 2.5011 | 39000 | 0.0536        |
+| 2.5332 | 39500 | 0.0559        |
+| 2.5653 | 40000 | 0.0524        |
+| 2.5973 | 40500 | 0.0496        |
+| 2.6294 | 41000 | 0.0486        |
+| 2.6615 | 41500 | 0.0526        |
+| 2.6935 | 42000 | 0.0443        |
+| 2.7256 | 42500 | 0.058         |
+| 2.7576 | 43000 | 0.0543        |
+| 2.7897 | 43500 | 0.0527        |
+| 2.8218 | 44000 | 0.0528        |
+| 2.8538 | 44500 | 0.0573        |
+| 2.8859 | 45000 | 0.0628        |
+| 2.9180 | 45500 | 0.0443        |
+| 2.9500 | 46000 | 0.0531        |
+| 2.9821 | 46500 | 0.0554        |
+| 3.0    | 46779 | -             |
+| 3.0142 | 47000 | 0.0346        |
+| 3.0462 | 47500 | 0.0288        |
+| 3.0783 | 48000 | 0.0219        |
+| 3.1104 | 48500 | 0.0259        |
+| 3.1424 | 49000 | 0.0237        |
+| 3.1745 | 49500 | 0.0307        |
+| 3.2066 | 50000 | 0.0234        |
+| 3.2386 | 50500 | 0.0312        |
+| 3.2707 | 51000 | 0.0297        |
+| 3.3028 | 51500 | 0.0299        |
+| 3.3348 | 52000 | 0.0326        |
+| 3.3669 | 52500 | 0.0266        |
+| 3.3990 | 53000 | 0.0296        |
+| 3.4310 | 53500 | 0.0289        |
+| 3.4631 | 54000 | 0.0216        |
+| 3.4952 | 54500 | 0.0289        |
+| 3.5272 | 55000 | 0.033         |
+| 3.5593 | 55500 | 0.0248        |
+| 3.5914 | 56000 | 0.0246        |
+| 3.6234 | 56500 | 0.0287        |
+| 3.6555 | 57000 | 0.0267        |
+| 3.6876 | 57500 | 0.0285        |
+| 3.7196 | 58000 | 0.0288        |
+| 3.7517 | 58500 | 0.0283        |
+| 3.7837 | 59000 | 0.0283        |
+| 3.8158 | 59500 | 0.029         |
+| 3.8479 | 60000 | 0.0327        |
+| 3.8799 | 60500 | 0.0239        |
+| 3.9120 | 61000 | 0.0356        |
+| 3.9441 | 61500 | 0.0323        |
+| 3.9761 | 62000 | 0.0213        |
+| 4.0    | 62372 | -             |
+| 4.0082 | 62500 | 0.0275        |
+| 4.0403 | 63000 | 0.0125        |
+| 4.0723 | 63500 | 0.0183        |
+| 4.1044 | 64000 | 0.0138        |
+| 4.1365 | 64500 | 0.0174        |
+| 4.1685 | 65000 | 0.0088        |
+| 4.2006 | 65500 | 0.0126        |
+| 4.2327 | 66000 | 0.0134        |
+| 4.2647 | 66500 | 0.0099        |
+| 4.2968 | 67000 | 0.0188        |
+| 4.3289 | 67500 | 0.0112        |
+| 4.3609 | 68000 | 0.0156        |
+| 4.3930 | 68500 | 0.0175        |
+| 4.4251 | 69000 | 0.0128        |
+| 4.4571 | 69500 | 0.0154        |
+| 4.4892 | 70000 | 0.0127        |
+| 4.5213 | 70500 | 0.0131        |
+| 4.5533 | 71000 | 0.017         |
+| 4.5854 | 71500 | 0.0116        |
+| 4.6175 | 72000 | 0.0137        |
+| 4.6495 | 72500 | 0.0156        |
+| 4.6816 | 73000 | 0.0155        |
+| 4.7137 | 73500 | 0.0078        |
+| 4.7457 | 74000 | 0.0152        |
+| 4.7778 | 74500 | 0.0089        |
+| 4.8099 | 75000 | 0.0116        |
+| 4.8419 | 75500 | 0.0144        |
+| 4.8740 | 76000 | 0.0112        |
+| 4.9060 | 76500 | 0.0108        |
+| 4.9381 | 77000 | 0.0188        |
+| 4.9702 | 77500 | 0.0109        |
+| 5.0    | 77965 | -             |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.2
+- 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",
+}
+```
+
+<!--
+## 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 +1,74 @@
+{
+  "_name_or_path": "nomic-ai/nomic-embed-text-v2-moe",
+  "activation_function": "gelu",
+  "add_pooling_layer": false,
+  "architectures": [
+    "NomicBertModel"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig",
+    "AutoModel": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel",
+    "AutoModelForMaskedLM": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForPreTraining",
+    "AutoModelForMultipleChoice": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForMultipleChoice",
+    "AutoModelForQuestionAnswering": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForQuestionAnswering",
+    "AutoModelForSequenceClassification": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForSequenceClassification",
+    "AutoModelForTokenClassification": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForTokenClassification"
+  },
+  "bos_token_id": null,
+  "causal": false,
+  "dense_seq_output": true,
+  "embd_pdrop": 0.1,
+  "eos_token_id": null,
+  "expert_choice_router": false,
+  "ffn_div": 1,
+  "fused_bias_fc": true,
+  "fused_dropout_add_ln": true,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_trained_positions": 2048,
+  "mlp_fc1_bias": true,
+  "mlp_fc2_bias": true,
+  "model_type": "nomic_bert",
+  "moe_every_n_layers": 2,
+  "moe_impl": "megablocks",
+  "moe_normalize_expert_weights": false,
+  "moe_resid_pdrop": 0.0,
+  "moe_top_k": 2,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 2048,
+  "num_experts": 8,
+  "num_shared_experts": 0,
+  "pad_token_id": 1,
+  "pad_vocab_size_multiple": 64,
+  "parallel_block": false,
+  "parallel_block_tied_norm": false,
+  "prenorm": false,
+  "qkv_proj_bias": true,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "rotary_emb_base": 10000,
+  "rotary_emb_fraction": 1.0,
+  "rotary_emb_interleaved": false,
+  "rotary_emb_scale_base": null,
+  "rotary_scaling_factor": null,
+  "router_aux_loss_coef": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "use_flash_attn": true,
+  "use_rms_norm": null,
+  "use_xentropy": true,
+  "vocab_size": 250048
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:149b21268186cf9c3033e99c44094a5195e553ab902e69a215ed0bb3dd150d84
+size 1901187232

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
+size 5069051

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa7a6ad87a7ce8fe196787355f6af7d03aee94d19c54a5eb1392ed18c8ef451a
+size 17082988

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}