Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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 +1,787 @@
+---
+base_model: Snowflake/snowflake-arctic-embed-xs
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:416298
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: The radial profiles using frank for the seven targets can be seen
+    in Figure 6.
+  sentences:
+  - At longer radio wavelengths, we selected the newest observations of the appropriate
+    resolution from the VLA archive.
+  - The radial profiles using frank for the seven targets can be seen in Figure 6.
+  - For further information on observation and data calibration, refer to Hunt et al.
+    (2021).
+- source_sentence: They are extragalactic scaled up versions of galactic Ultra Compact
+    (UC) H ii regions, which are typically excited by a single massive star and are
+    ≲less-than-or-similar-to\lesssim 0.1 pc in size (Wood & Churchwell, 1989).
+  sentences:
+  - They are extragalactic scaled up versions of galactic Ultra Compact (UC) H ii
+    regions, which are typically excited by a single massive star and are ≲less-than-or-similar-to\lesssim
+    0.1 pc in size (Wood & Churchwell, 1989).
+  - The LMT is a project operated by the Instituto Nacional de Astrófisica, Óptica,
+    y Electrónica (Mexico) and the University of Massachusetts at Amherst (USA).
+  - We measure the detection confidence in the resolved image as the ratio between
+    the local mean posterior and the local posterior standard deviation of the estimated
+    circular polarization, evaluated based on 1000 images drawn from the posterior
+    distribution.
+- source_sentence: The flux density calibrator was 3C286, and the complex gain calibrator
+    was J0836-2016.
+  sentences:
+  - The flux density calibrator was 3C286, and the complex gain calibrator was J0836-2016.
+  - While rcsubscript𝑟cr_{\rm c} has a clear dependence on Dmaxsubscript𝐷maxD_{\rm
+    max}, xMMSNsubscript𝑥MMSNx_{\rm MMSN} and tagesubscript𝑡aget_{\rm age}, ΣcsubscriptΣc\Sigma_{\rm
+    c} only has weak dependence on Dmaxsubscript𝐷maxD_{\rm max}, and so is mostly
+    sensitive to the scaling of the total initial planetesimal mass, xMMSNsubscript𝑥MMSNx_{\rm
+    MMSN} and tagesubscript𝑡aget_{\rm age}.
+  - 20 is valid only at r=rc𝑟subscript𝑟cr=r_{\rm c}, it has been shown that the surface
+    density of dust at r>rc𝑟subscript𝑟cr>r_{\rm c} is expected to be flat for a primordial
+    surface density exponent (−α𝛼-\alpha) of -3/2, or more generally proportional
+    to r−0.6​α+0.9superscript𝑟0.6𝛼0.9r^{-0.6\alpha+0.9} (Schüppler et al., 2016; Marino
+    et al., 2017b; Geiler & Krivov, 2017).
+- source_sentence: We would like to thank A. Deller and W. Brisken for EHT-specific
+    support with the use of DiFX.
+  sentences:
+  - Ice has one of the weakest strengths, and thus if we had assumed stronger solids
+    the derived values of Dmaxsubscript𝐷D_{\max} and xMMSNsubscript𝑥MMSNx_{\rm MMSN}
+    would be lower.
+  - We would like to thank A. Deller and W. Brisken for EHT-specific support with
+    the use of DiFX.
+  - The wsmoothsubscript𝑤smoothw_{\rm smooth} chosen parameter ranged from 10−2superscript10210^{-2}
+    to 10−4superscript10410^{-4} depending on the disc.
+- source_sentence: New higher resolution images and our parametric modelling confirmed
+    this finding.
+  sentences:
+  - New higher resolution images and our parametric modelling confirmed this finding.
+  - With the 3-bit correlator configuration, we obtained a total bandwidth of ∼similar-to\sim8 GHz
+    across Ka-band.
+  - Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.
+---
+
+# internstall-ice-crystal-xs
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) <!-- at revision 742da4f66e1823b5b4dbe6c320a1375a1fd85f9e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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("SimoneAstarita/interstellar-ice-crystal-xs")
+# Run inference
+sentences = [
+    'New higher resolution images and our parametric modelling confirmed this finding.',
+    'New higher resolution images and our parametric modelling confirmed this finding.',
+    'Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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: 416,298 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 42.81 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 42.81 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                               | positive                                                                                                                                                             |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope</code>                                                                    | <code>Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope</code>                                                                    |
+  | <code>The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).</code> | <code>The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).</code> |
+  | <code>The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).</code>                | <code>The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).</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
+
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: no
+- `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`: 1.0
+- `num_train_epochs`: 3
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0077 | 100   | 0.4784        |
+| 0.0154 | 200   | 0.2415        |
+| 0.0231 | 300   | 0.0424        |
+| 0.0307 | 400   | 0.021         |
+| 0.0384 | 500   | 0.0149        |
+| 0.0461 | 600   | 0.0081        |
+| 0.0538 | 700   | 0.0084        |
+| 0.0615 | 800   | 0.0067        |
+| 0.0692 | 900   | 0.0034        |
+| 0.0769 | 1000  | 0.0025        |
+| 0.0846 | 1100  | 0.0016        |
+| 0.0077 | 100   | 0.0025        |
+| 0.0154 | 200   | 0.0032        |
+| 0.0231 | 300   | 0.0026        |
+| 0.0307 | 400   | 0.0026        |
+| 0.0384 | 500   | 0.0041        |
+| 0.0461 | 600   | 0.0014        |
+| 0.0538 | 700   | 0.0019        |
+| 0.0615 | 800   | 0.0015        |
+| 0.0692 | 900   | 0.001         |
+| 0.0769 | 1000  | 0.0005        |
+| 0.0846 | 1100  | 0.0004        |
+| 0.0922 | 1200  | 0.0013        |
+| 0.0999 | 1300  | 0.0013        |
+| 0.1076 | 1400  | 0.0027        |
+| 0.1153 | 1500  | 0.0018        |
+| 0.1230 | 1600  | 0.001         |
+| 0.1307 | 1700  | 0.0014        |
+| 0.1384 | 1800  | 0.0012        |
+| 0.1460 | 1900  | 0.0041        |
+| 0.1537 | 2000  | 0.0009        |
+| 0.1614 | 2100  | 0.0005        |
+| 0.1691 | 2200  | 0.0011        |
+| 0.1768 | 2300  | 0.001         |
+| 0.1845 | 2400  | 0.0004        |
+| 0.1922 | 2500  | 0.0011        |
+| 0.1998 | 2600  | 0.0044        |
+| 0.2075 | 2700  | 0.0004        |
+| 0.2152 | 2800  | 0.0022        |
+| 0.2229 | 2900  | 0.0007        |
+| 0.2306 | 3000  | 0.0006        |
+| 0.2383 | 3100  | 0.0002        |
+| 0.2460 | 3200  | 0.0006        |
+| 0.2537 | 3300  | 0.0004        |
+| 0.2613 | 3400  | 0.0013        |
+| 0.2690 | 3500  | 0.0006        |
+| 0.2767 | 3600  | 0.0005        |
+| 0.2844 | 3700  | 0.0018        |
+| 0.2921 | 3800  | 0.0023        |
+| 0.2998 | 3900  | 0.0011        |
+| 0.3075 | 4000  | 0.0007        |
+| 0.3151 | 4100  | 0.0008        |
+| 0.3228 | 4200  | 0.0013        |
+| 0.3305 | 4300  | 0.0012        |
+| 0.3382 | 4400  | 0.001         |
+| 0.3459 | 4500  | 0.0016        |
+| 0.3536 | 4600  | 0.0025        |
+| 0.3613 | 4700  | 0.0015        |
+| 0.3689 | 4800  | 0.0018        |
+| 0.3766 | 4900  | 0.0019        |
+| 0.3843 | 5000  | 0.0021        |
+| 0.3920 | 5100  | 0.0018        |
+| 0.3997 | 5200  | 0.0004        |
+| 0.4074 | 5300  | 0.0006        |
+| 0.4151 | 5400  | 0.0007        |
+| 0.4228 | 5500  | 0.0009        |
+| 0.4304 | 5600  | 0.0004        |
+| 0.4381 | 5700  | 0.0003        |
+| 0.4458 | 5800  | 0.0007        |
+| 0.4535 | 5900  | 0.0013        |
+| 0.4612 | 6000  | 0.0007        |
+| 0.4689 | 6100  | 0.0005        |
+| 0.4766 | 6200  | 0.001         |
+| 0.4842 | 6300  | 0.0027        |
+| 0.4919 | 6400  | 0.0018        |
+| 0.4996 | 6500  | 0.0006        |
+| 0.5073 | 6600  | 0.0008        |
+| 0.5150 | 6700  | 0.0006        |
+| 0.5227 | 6800  | 0.0007        |
+| 0.5304 | 6900  | 0.001         |
+| 0.5380 | 7000  | 0.0007        |
+| 0.5457 | 7100  | 0.0005        |
+| 0.5534 | 7200  | 0.0012        |
+| 0.5611 | 7300  | 0.0012        |
+| 0.5688 | 7400  | 0.0011        |
+| 0.5765 | 7500  | 0.0005        |
+| 0.5842 | 7600  | 0.0013        |
+| 0.5919 | 7700  | 0.0012        |
+| 0.5995 | 7800  | 0.0007        |
+| 0.6072 | 7900  | 0.0012        |
+| 0.6149 | 8000  | 0.0012        |
+| 0.6226 | 8100  | 0.0003        |
+| 0.6303 | 8200  | 0.0003        |
+| 0.6380 | 8300  | 0.0003        |
+| 0.6457 | 8400  | 0.002         |
+| 0.6533 | 8500  | 0.0003        |
+| 0.6610 | 8600  | 0.0016        |
+| 0.6687 | 8700  | 0.0003        |
+| 0.6764 | 8800  | 0.0002        |
+| 0.6841 | 8900  | 0.0006        |
+| 0.6918 | 9000  | 0.0005        |
+| 0.6995 | 9100  | 0.0017        |
+| 0.7071 | 9200  | 0.0037        |
+| 0.7148 | 9300  | 0.0005        |
+| 0.7225 | 9400  | 0.0006        |
+| 0.7302 | 9500  | 0.0004        |
+| 0.7379 | 9600  | 0.0002        |
+| 0.7456 | 9700  | 0.0008        |
+| 0.7533 | 9800  | 0.0005        |
+| 0.7610 | 9900  | 0.0006        |
+| 0.7686 | 10000 | 0.0004        |
+| 0.7763 | 10100 | 0.0004        |
+| 0.7840 | 10200 | 0.0006        |
+| 0.7917 | 10300 | 0.0019        |
+| 0.7994 | 10400 | 0.0007        |
+| 0.8071 | 10500 | 0.0003        |
+| 0.8148 | 10600 | 0.0003        |
+| 0.8224 | 10700 | 0.0005        |
+| 0.8301 | 10800 | 0.0009        |
+| 0.8378 | 10900 | 0.0006        |
+| 0.8455 | 11000 | 0.002         |
+| 0.8532 | 11100 | 0.0018        |
+| 0.8609 | 11200 | 0.0009        |
+| 0.8686 | 11300 | 0.0004        |
+| 0.8762 | 11400 | 0.0005        |
+| 0.8839 | 11500 | 0.0008        |
+| 0.8916 | 11600 | 0.0003        |
+| 0.8993 | 11700 | 0.0002        |
+| 0.9070 | 11800 | 0.0004        |
+| 0.9147 | 11900 | 0.0007        |
+| 0.9224 | 12000 | 0.0009        |
+| 0.9301 | 12100 | 0.0007        |
+| 0.9377 | 12200 | 0.0007        |
+| 0.9454 | 12300 | 0.0009        |
+| 0.9531 | 12400 | 0.0007        |
+| 0.9608 | 12500 | 0.0009        |
+| 0.9685 | 12600 | 0.0004        |
+| 0.9762 | 12700 | 0.0002        |
+| 0.9839 | 12800 | 0.0003        |
+| 0.9915 | 12900 | 0.0002        |
+| 0.9992 | 13000 | 0.0002        |
+| 1.0069 | 13100 | 0.0006        |
+| 1.0146 | 13200 | 0.0007        |
+| 1.0223 | 13300 | 0.0007        |
+| 1.0300 | 13400 | 0.0005        |
+| 1.0377 | 13500 | 0.0008        |
+| 1.0453 | 13600 | 0.0016        |
+| 1.0530 | 13700 | 0.0007        |
+| 1.0607 | 13800 | 0.0013        |
+| 1.0684 | 13900 | 0.0005        |
+| 1.0761 | 14000 | 0.0002        |
+| 1.0838 | 14100 | 0.0001        |
+| 1.0915 | 14200 | 0.0003        |
+| 1.0992 | 14300 | 0.0003        |
+| 1.1068 | 14400 | 0.0006        |
+| 1.1145 | 14500 | 0.0002        |
+| 1.1222 | 14600 | 0.0003        |
+| 1.1299 | 14700 | 0.0002        |
+| 1.1376 | 14800 | 0.0006        |
+| 1.1453 | 14900 | 0.0011        |
+| 1.1530 | 15000 | 0.0004        |
+| 1.1606 | 15100 | 0.0001        |
+| 1.1683 | 15200 | 0.0003        |
+| 1.1760 | 15300 | 0.0001        |
+| 1.1837 | 15400 | 0.0002        |
+| 1.1914 | 15500 | 0.0001        |
+| 1.1991 | 15600 | 0.003         |
+| 1.2068 | 15700 | 0.0001        |
+| 1.2145 | 15800 | 0.0002        |
+| 1.2221 | 15900 | 0.0005        |
+| 1.2298 | 16000 | 0.0004        |
+| 1.2375 | 16100 | 0.0001        |
+| 1.2452 | 16200 | 0.0003        |
+| 1.2529 | 16300 | 0.0003        |
+| 1.2606 | 16400 | 0.0008        |
+| 1.2683 | 16500 | 0.0004        |
+| 1.2759 | 16600 | 0.0001        |
+| 1.2836 | 16700 | 0.0002        |
+| 1.2913 | 16800 | 0.0011        |
+| 1.2990 | 16900 | 0.0001        |
+| 1.3067 | 17000 | 0.0001        |
+| 1.3144 | 17100 | 0.0002        |
+| 1.3221 | 17200 | 0.0005        |
+| 1.3297 | 17300 | 0.0012        |
+| 1.3374 | 17400 | 0.0003        |
+| 1.3451 | 17500 | 0.0002        |
+| 1.3528 | 17600 | 0.0009        |
+| 1.3605 | 17700 | 0.0003        |
+| 1.3682 | 17800 | 0.0005        |
+| 1.3759 | 17900 | 0.0008        |
+| 1.3836 | 18000 | 0.0005        |
+| 1.3912 | 18100 | 0.0007        |
+| 1.3989 | 18200 | 0.0002        |
+| 1.4066 | 18300 | 0.0003        |
+| 1.4143 | 18400 | 0.0002        |
+| 1.4220 | 18500 | 0.0001        |
+| 1.4297 | 18600 | 0.0001        |
+| 1.4374 | 18700 | 0.0001        |
+| 1.4450 | 18800 | 0.0005        |
+| 1.4527 | 18900 | 0.0002        |
+| 1.4604 | 19000 | 0.0001        |
+| 1.4681 | 19100 | 0.0002        |
+| 1.4758 | 19200 | 0.0006        |
+| 1.4835 | 19300 | 0.0015        |
+| 1.4912 | 19400 | 0.0012        |
+| 1.4988 | 19500 | 0.0003        |
+| 1.5065 | 19600 | 0.0005        |
+| 1.5142 | 19700 | 0.0001        |
+| 1.5219 | 19800 | 0.0002        |
+| 1.5296 | 19900 | 0.0009        |
+| 1.5373 | 20000 | 0.0002        |
+| 1.5450 | 20100 | 0.0001        |
+| 1.5527 | 20200 | 0.0003        |
+| 1.5603 | 20300 | 0.0006        |
+| 1.5680 | 20400 | 0.0002        |
+| 1.5757 | 20500 | 0.0004        |
+| 1.5834 | 20600 | 0.0006        |
+| 1.5911 | 20700 | 0.0004        |
+| 1.5988 | 20800 | 0.0002        |
+| 1.6065 | 20900 | 0.0006        |
+| 1.6141 | 21000 | 0.0006        |
+| 1.6218 | 21100 | 0.0001        |
+| 1.6295 | 21200 | 0.0001        |
+| 1.6372 | 21300 | 0.0001        |
+| 1.6449 | 21400 | 0.0008        |
+| 1.6526 | 21500 | 0.0001        |
+| 1.6603 | 21600 | 0.0005        |
+| 1.6679 | 21700 | 0.0001        |
+| 1.6756 | 21800 | 0.0001        |
+| 1.6833 | 21900 | 0.0001        |
+| 1.6910 | 22000 | 0.0001        |
+| 1.6987 | 22100 | 0.0008        |
+| 1.7064 | 22200 | 0.0014        |
+| 1.7141 | 22300 | 0.0002        |
+| 1.7218 | 22400 | 0.0007        |
+| 1.7294 | 22500 | 0.0001        |
+| 1.7371 | 22600 | 0.0001        |
+| 1.7448 | 22700 | 0.0001        |
+| 1.7525 | 22800 | 0.0002        |
+| 1.7602 | 22900 | 0.0002        |
+| 1.7679 | 23000 | 0.0001        |
+| 1.7756 | 23100 | 0.0001        |
+| 1.7832 | 23200 | 0.0005        |
+| 1.7909 | 23300 | 0.0004        |
+| 1.7986 | 23400 | 0.0002        |
+| 1.8063 | 23500 | 0.0001        |
+| 1.8140 | 23600 | 0.0001        |
+| 1.8217 | 23700 | 0.0001        |
+| 1.8294 | 23800 | 0.0004        |
+| 1.8370 | 23900 | 0.0002        |
+| 1.8447 | 24000 | 0.0002        |
+| 1.8524 | 24100 | 0.0013        |
+| 1.8601 | 24200 | 0.0004        |
+| 1.8678 | 24300 | 0.0002        |
+| 1.8755 | 24400 | 0.0002        |
+| 1.8832 | 24500 | 0.0001        |
+| 1.8909 | 24600 | 0.0001        |
+| 1.8985 | 24700 | 0.0001        |
+| 1.9062 | 24800 | 0.0002        |
+| 1.9139 | 24900 | 0.0005        |
+| 1.9216 | 25000 | 0.0001        |
+| 1.9293 | 25100 | 0.0001        |
+| 1.9370 | 25200 | 0.0002        |
+| 1.9447 | 25300 | 0.0002        |
+| 1.9523 | 25400 | 0.0006        |
+| 1.9600 | 25500 | 0.0004        |
+| 1.9677 | 25600 | 0.0002        |
+| 1.9754 | 25700 | 0.0001        |
+| 1.9831 | 25800 | 0.0001        |
+| 1.9908 | 25900 | 0.0001        |
+| 1.9985 | 26000 | 0.0001        |
+| 2.0061 | 26100 | 0.0002        |
+| 2.0138 | 26200 | 0.0007        |
+| 2.0215 | 26300 | 0.0003        |
+| 2.0292 | 26400 | 0.0001        |
+| 2.0369 | 26500 | 0.0011        |
+| 2.0446 | 26600 | 0.0002        |
+| 2.0523 | 26700 | 0.0001        |
+| 2.0600 | 26800 | 0.0002        |
+| 2.0676 | 26900 | 0.0004        |
+| 2.0753 | 27000 | 0.0001        |
+| 2.0830 | 27100 | 0.0001        |
+| 2.0907 | 27200 | 0.0001        |
+| 2.0984 | 27300 | 0.0002        |
+| 2.1061 | 27400 | 0.0001        |
+| 2.1138 | 27500 | 0.0001        |
+| 2.1214 | 27600 | 0.0001        |
+| 2.1291 | 27700 | 0.0001        |
+| 2.1368 | 27800 | 0.0003        |
+| 2.1445 | 27900 | 0.0012        |
+| 2.1522 | 28000 | 0.0001        |
+| 2.1599 | 28100 | 0.0001        |
+| 2.1676 | 28200 | 0.0001        |
+| 2.1752 | 28300 | 0.0001        |
+| 2.1829 | 28400 | 0.0001        |
+| 2.1906 | 28500 | 0.0001        |
+| 2.1983 | 28600 | 0.0014        |
+| 2.2060 | 28700 | 0.0001        |
+| 2.2137 | 28800 | 0.0001        |
+| 2.2214 | 28900 | 0.0002        |
+| 2.2291 | 29000 | 0.0           |
+| 2.2367 | 29100 | 0.0001        |
+| 2.2444 | 29200 | 0.0001        |
+| 2.2521 | 29300 | 0.0001        |
+| 2.2598 | 29400 | 0.0001        |
+| 2.2675 | 29500 | 0.0001        |
+| 2.2752 | 29600 | 0.0001        |
+| 2.2829 | 29700 | 0.0001        |
+| 2.2905 | 29800 | 0.0001        |
+| 2.2982 | 29900 | 0.0001        |
+| 2.3059 | 30000 | 0.0001        |
+| 2.3136 | 30100 | 0.0001        |
+| 2.3213 | 30200 | 0.0002        |
+| 2.3290 | 30300 | 0.0011        |
+| 2.3367 | 30400 | 0.0001        |
+| 2.3444 | 30500 | 0.0001        |
+| 2.3520 | 30600 | 0.0005        |
+| 2.3597 | 30700 | 0.0001        |
+| 2.3674 | 30800 | 0.0001        |
+| 2.3751 | 30900 | 0.0006        |
+| 2.3828 | 31000 | 0.0001        |
+| 2.3905 | 31100 | 0.0001        |
+| 2.3982 | 31200 | 0.0002        |
+| 2.4058 | 31300 | 0.0001        |
+| 2.4135 | 31400 | 0.0001        |
+| 2.4212 | 31500 | 0.0001        |
+| 2.4289 | 31600 | 0.0001        |
+| 2.4366 | 31700 | 0.0001        |
+| 2.4443 | 31800 | 0.0004        |
+| 2.4520 | 31900 | 0.0001        |
+| 2.4596 | 32000 | 0.0001        |
+| 2.4673 | 32100 | 0.0002        |
+| 2.4750 | 32200 | 0.0002        |
+| 2.4827 | 32300 | 0.0004        |
+| 2.4904 | 32400 | 0.0008        |
+| 2.4981 | 32500 | 0.0001        |
+| 2.5058 | 32600 | 0.0001        |
+| 2.5135 | 32700 | 0.0001        |
+| 2.5211 | 32800 | 0.0001        |
+| 2.5288 | 32900 | 0.0006        |
+| 2.5365 | 33000 | 0.0001        |
+| 2.5442 | 33100 | 0.0001        |
+| 2.5519 | 33200 | 0.0002        |
+| 2.5596 | 33300 | 0.0001        |
+| 2.5673 | 33400 | 0.0002        |
+| 2.5749 | 33500 | 0.0001        |
+| 2.5826 | 33600 | 0.0001        |
+| 2.5903 | 33700 | 0.0001        |
+| 2.5980 | 33800 | 0.0001        |
+| 2.6057 | 33900 | 0.0001        |
+| 2.6134 | 34000 | 0.0007        |
+| 2.6211 | 34100 | 0.0           |
+| 2.6287 | 34200 | 0.0001        |
+| 2.6364 | 34300 | 0.0001        |
+| 2.6441 | 34400 | 0.0006        |
+| 2.6518 | 34500 | 0.0001        |
+| 2.6595 | 34600 | 0.0001        |
+| 2.6672 | 34700 | 0.0001        |
+| 2.6749 | 34800 | 0.0           |
+| 2.6826 | 34900 | 0.0001        |
+| 2.6902 | 35000 | 0.0001        |
+| 2.6979 | 35100 | 0.0005        |
+| 2.7056 | 35200 | 0.0006        |
+| 2.7133 | 35300 | 0.0001        |
+| 2.7210 | 35400 | 0.0005        |
+| 2.7287 | 35500 | 0.0001        |
+| 2.7364 | 35600 | 0.0001        |
+| 2.7440 | 35700 | 0.0001        |
+| 2.7517 | 35800 | 0.0001        |
+| 2.7594 | 35900 | 0.0001        |
+| 2.7671 | 36000 | 0.0001        |
+| 2.7748 | 36100 | 0.0001        |
+| 2.7825 | 36200 | 0.0005        |
+| 2.7902 | 36300 | 0.0001        |
+| 2.7978 | 36400 | 0.0001        |
+| 2.8055 | 36500 | 0.0001        |
+| 2.8132 | 36600 | 0.0001        |
+| 2.8209 | 36700 | 0.0001        |
+| 2.8286 | 36800 | 0.0001        |
+| 2.8363 | 36900 | 0.0001        |
+| 2.8440 | 37000 | 0.0001        |
+| 2.8517 | 37100 | 0.0013        |
+| 2.8593 | 37200 | 0.0001        |
+| 2.8670 | 37300 | 0.0001        |
+| 2.8747 | 37400 | 0.0001        |
+| 2.8824 | 37500 | 0.0001        |
+| 2.8901 | 37600 | 0.0001        |
+| 2.8978 | 37700 | 0.0001        |
+| 2.9055 | 37800 | 0.0001        |
+| 2.9131 | 37900 | 0.0002        |
+| 2.9208 | 38000 | 0.0001        |
+| 2.9285 | 38100 | 0.0001        |
+| 2.9362 | 38200 | 0.0001        |
+| 2.9439 | 38300 | 0.0001        |
+| 2.9516 | 38400 | 0.0004        |
+| 2.9593 | 38500 | 0.0001        |
+| 2.9669 | 38600 | 0.0001        |
+| 2.9746 | 38700 | 0.0001        |
+| 2.9823 | 38800 | 0.0001        |
+| 2.9900 | 38900 | 0.0001        |
+| 2.9977 | 39000 | 0.0001        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.0+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- Tokenizers: 0.19.1
+
+## 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 +1,26 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-xs",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05498bd7198e3b319b687e7445c016ab688016f73772bef0244bd55999f16472
+size 90272656

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": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}