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

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+{
+  "word_embedding_dimension": 1024,
+  "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 +1,535 @@
+---
+language:
+- en
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:557850
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l-v2.0
+widget:
+- source_sentence: A construction worker is standing on a crane placing a large arm
+    on top of a stature in progress.
+  sentences:
+  - A man is playing with his camera.
+  - A person standing
+  - Nobody is standing
+- source_sentence: A boy in red slides down an inflatable ride.
+  sentences:
+  - a baby smiling
+  - A boy is playing on an inflatable ride.
+  - A boy pierces a knife through an inflatable ride.
+- source_sentence: A man in a black shirt is playing a guitar.
+  sentences:
+  - A group of women are selling their wares
+  - The man is wearing black.
+  - The man is wearing a blue shirt.
+- source_sentence: A man with a large power drill standing next to his daughter with
+    a vacuum cleaner hose.
+  sentences:
+  - A man holding a drill stands next to a girl holding a vacuum hose.
+  - Kids ride an amusement ride.
+  - The man and girl are painting the walls.
+- source_sentence: A middle-aged man works under the engine of a train on rail tracks.
+  sentences:
+  - A guy is working on a train.
+  - Two young asian men are squatting.
+  - A guy is driving to work.
+datasets:
+- sentence-transformers/all-nli
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test
+      type: all-nli-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9558178241791496
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) <!-- at revision 7f311bb640ad3babc0a4e3a8873240dcba44c9d2 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
+- **Language:** en
+<!-- - **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: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+  (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("JatinkInnovision/snowflake-arctic-embed-l-v2.0_all-nli")
+# Run inference
+sentences = [
+    'A middle-aged man works under the engine of a train on rail tracks.',
+    'A guy is working on a train.',
+    'A guy is driving to work.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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
+
+* Dataset: `all-nli-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.9558** |
+
+<!--
+## 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
+
+#### all-nli
+
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 557,850 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 10.9 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                         | negative                                                   |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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
+
+#### all-nli
+
+* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 6,584 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 20.31 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
+  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
+  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
+  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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`: 50
+- `per_device_eval_batch_size`: 50
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `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`: 50
+- `per_device_eval_batch_size`: 50
+- `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`: 1.0
+- `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`: 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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss | Validation Loss | all-nli-test_cosine_accuracy |
+|:------:|:-----:|:-------------:|:---------------:|:----------------------------:|
+| 0.0090 | 100   | 1.8838        | 0.6502          | -                            |
+| 0.0179 | 200   | 1.2991        | 0.6177          | -                            |
+| 0.0269 | 300   | 1.2721        | 0.6417          | -                            |
+| 0.0359 | 400   | 1.2265        | 0.7053          | -                            |
+| 0.0448 | 500   | 1.0111        | 0.7147          | -                            |
+| 0.0538 | 600   | 1.0491        | 0.7457          | -                            |
+| 0.0627 | 700   | 1.0186        | 0.7922          | -                            |
+| 0.0717 | 800   | 1.135         | 0.8940          | -                            |
+| 0.0807 | 900   | 1.0747        | 0.7007          | -                            |
+| 0.0896 | 1000  | 0.9373        | 0.7298          | -                            |
+| 0.0986 | 1100  | 0.9572        | 0.6809          | -                            |
+| 0.1076 | 1200  | 1.1316        | 0.7260          | -                            |
+| 0.1165 | 1300  | 0.9188        | 0.7085          | -                            |
+| 0.1255 | 1400  | 0.9554        | 0.6876          | -                            |
+| 0.1344 | 1500  | 0.9494        | 0.7492          | -                            |
+| 0.1434 | 1600  | 0.811         | 0.7234          | -                            |
+| 0.1524 | 1700  | 0.7766        | 0.6744          | -                            |
+| 0.1613 | 1800  | 0.9317        | 0.7178          | -                            |
+| 0.1703 | 1900  | 0.9148        | 0.6960          | -                            |
+| 0.1793 | 2000  | 0.8643        | 0.6642          | -                            |
+| 0.1882 | 2100  | 0.7604        | 0.6425          | -                            |
+| 0.1972 | 2200  | 0.776         | 0.6347          | -                            |
+| 0.2061 | 2300  | 0.8286        | 0.6581          | -                            |
+| 0.2151 | 2400  | 0.8946        | 0.5866          | -                            |
+| 0.2241 | 2500  | 0.8507        | 0.6845          | -                            |
+| 0.2330 | 2600  | 0.7917        | 0.6091          | -                            |
+| 0.2420 | 2700  | 0.8192        | 0.7073          | -                            |
+| 0.2510 | 2800  | 0.8818        | 0.6584          | -                            |
+| 0.2599 | 2900  | 0.8261        | 0.6112          | -                            |
+| 0.2689 | 3000  | 0.8017        | 0.6883          | -                            |
+| 0.2779 | 3100  | 0.8147        | 0.6450          | -                            |
+| 0.2868 | 3200  | 0.8297        | 0.6086          | -                            |
+| 0.2958 | 3300  | 0.7516        | 0.5857          | -                            |
+| 0.3047 | 3400  | 0.8628        | 0.6061          | -                            |
+| 0.3137 | 3500  | 0.7758        | 0.5751          | -                            |
+| 0.3227 | 3600  | 0.7773        | 0.6022          | -                            |
+| 0.3316 | 3700  | 0.7559        | 0.5446          | -                            |
+| 0.3406 | 3800  | 0.796         | 0.5842          | -                            |
+| 0.3496 | 3900  | 0.8295        | 0.5822          | -                            |
+| 0.3585 | 4000  | 0.7292        | 0.5821          | -                            |
+| 0.3675 | 4100  | 0.7475        | 0.6358          | -                            |
+| 0.3764 | 4200  | 0.7916        | 0.5688          | -                            |
+| 0.3854 | 4300  | 0.7214        | 0.5653          | -                            |
+| 0.3944 | 4400  | 0.704         | 0.5564          | -                            |
+| 0.4033 | 4500  | 0.7817        | 0.5876          | -                            |
+| 0.4123 | 4600  | 0.7549        | 0.5358          | -                            |
+| 0.4213 | 4700  | 0.7206        | 0.5785          | -                            |
+| 0.4302 | 4800  | 0.7462        | 0.5568          | -                            |
+| 0.4392 | 4900  | 0.665         | 0.5765          | -                            |
+| 0.4481 | 5000  | 0.7743        | 0.5303          | -                            |
+| 0.4571 | 5100  | 0.7055        | 0.5733          | -                            |
+| 0.4661 | 5200  | 0.7004        | 0.6280          | -                            |
+| 0.4750 | 5300  | 0.7021        | 0.5444          | -                            |
+| 0.4840 | 5400  | 0.6858        | 0.5787          | -                            |
+| 0.4930 | 5500  | 0.7007        | 0.6124          | -                            |
+| 0.5019 | 5600  | 0.6722        | 0.5705          | -                            |
+| 0.5109 | 5700  | 0.7124        | 0.5440          | -                            |
+| 0.5199 | 5800  | 0.6657        | 0.5262          | -                            |
+| 0.5288 | 5900  | 0.6784        | 0.5400          | -                            |
+| 0.5378 | 6000  | 0.6644        | 0.5093          | -                            |
+| 0.5467 | 6100  | 0.7195        | 0.5453          | -                            |
+| 0.5557 | 6200  | 0.6958        | 0.5216          | -                            |
+| 0.5647 | 6300  | 0.7202        | 0.5250          | -                            |
+| 0.5736 | 6400  | 0.6921        | 0.5089          | -                            |
+| 0.5826 | 6500  | 0.6926        | 0.5207          | -                            |
+| 0.5916 | 6600  | 0.714         | 0.5084          | -                            |
+| 0.6005 | 6700  | 0.6605        | 0.4943          | -                            |
+| 0.6095 | 6800  | 0.7222        | 0.5058          | -                            |
+| 0.6184 | 6900  | 0.7171        | 0.4950          | -                            |
+| 0.6274 | 7000  | 0.6344        | 0.5110          | -                            |
+| 0.6364 | 7100  | 0.7057        | 0.5197          | -                            |
+| 0.6453 | 7200  | 0.6895        | 0.5096          | -                            |
+| 0.6543 | 7300  | 0.7226        | 0.4819          | -                            |
+| 0.6633 | 7400  | 0.6725        | 0.4780          | -                            |
+| 0.6722 | 7500  | 0.7469        | 0.5145          | -                            |
+| 0.6812 | 7600  | 0.7016        | 0.4969          | -                            |
+| 0.6901 | 7700  | 0.6655        | 0.4965          | -                            |
+| 0.6991 | 7800  | 0.7281        | 0.4913          | -                            |
+| 0.7081 | 7900  | 0.6748        | 0.5121          | -                            |
+| 0.7170 | 8000  | 0.6505        | 0.5207          | -                            |
+| 0.7260 | 8100  | 0.6594        | 0.4823          | -                            |
+| 0.7350 | 8200  | 0.7042        | 0.4903          | -                            |
+| 0.7439 | 8300  | 0.6995        | 0.4630          | -                            |
+| 0.7529 | 8400  | 0.634         | 0.4217          | -                            |
+| 0.7619 | 8500  | 0.3772        | 0.3684          | -                            |
+| 0.7708 | 8600  | 0.3416        | 0.3585          | -                            |
+| 0.7798 | 8700  | 0.3113        | 0.3471          | -                            |
+| 0.7887 | 8800  | 0.2793        | 0.3379          | -                            |
+| 0.7977 | 8900  | 0.2577        | 0.3349          | -                            |
+| 0.8067 | 9000  | 0.249         | 0.3320          | -                            |
+| 0.8156 | 9100  | 0.2191        | 0.3290          | -                            |
+| 0.8246 | 9200  | 0.2492        | 0.3255          | -                            |
+| 0.8336 | 9300  | 0.2464        | 0.3258          | -                            |
+| 0.8425 | 9400  | 0.2288        | 0.3247          | -                            |
+| 0.8515 | 9500  | 0.2132        | 0.3248          | -                            |
+| 0.8604 | 9600  | 0.2173        | 0.3259          | -                            |
+| 0.8694 | 9700  | 0.2008        | 0.3223          | -                            |
+| 0.8784 | 9800  | 0.2016        | 0.3219          | -                            |
+| 0.8873 | 9900  | 0.1962        | 0.3195          | -                            |
+| 0.8963 | 10000 | 0.1952        | 0.3185          | -                            |
+| 0.9053 | 10100 | 0.1959        | 0.3158          | -                            |
+| 0.9142 | 10200 | 0.2002        | 0.3138          | -                            |
+| 0.9232 | 10300 | 0.1882        | 0.3150          | -                            |
+| 0.9322 | 10400 | 0.1856        | 0.3124          | -                            |
+| 0.9411 | 10500 | 0.1971        | 0.3143          | -                            |
+| 0.9501 | 10600 | 0.1918        | 0.3137          | -                            |
+| 0.9590 | 10700 | 0.1825        | 0.3147          | -                            |
+| 0.9680 | 10800 | 0.1762        | 0.3155          | -                            |
+| 0.9770 | 10900 | 0.1778        | 0.3139          | -                            |
+| 0.9859 | 11000 | 0.1659        | 0.3138          | -                            |
+| 0.9949 | 11100 | 0.1848        | 0.3131          | -                            |
+| 1.0    | 11157 | -             | -               | 0.9558                       |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- 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 +1,28 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-l-v2.0",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {
+    "query": "query: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d3f550c56cebd317bb0dddafde1f561bde4a2b6adbde651773771d624003046e
+size 2271064456

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"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+  "eos_token": {
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+    "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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
+size 17083053

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": true,
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+      "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>",
+  "max_length": 512,
+  "model_max_length": 8192,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}