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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:46716
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Structurally, diplomonads have two equal-sized what and multiple
+    flagella?
+  sentences:
+  - deciding when to buy or sell a stock is not an easy task because the market is
+    hard to predict, being influenced by political and economic factors. thus, methodologies
+    based on computational intelligence have been applied to this challenging problem.
+    in this work, every day the stocks are ranked by technique for order preference
+    by similarity to ideal solution ( topsis ) using technical analysis criteria,
+    and the most suitable stock is selected for purchase. even so, it may occur that
+    the market is not favorable to purchase on certain days, or even, the topsis make
+    an incorrect selection. to improve the selection, another method should be used.
+    so, a hybrid
+  - 'we present the analysis of the brightest flare that was recorded in the \ emph
+    { insight } - hmxt data set, in a broad energy range ( 2 $ - $ 200 kev ) from
+    the microquasar grs ~ 1915 + 105 during an unusual low - luminosity state. this
+    flare was detected by \ emph { insight } - hxmt among a series of flares during
+    2 june 2019 utc 16 : 37 : 06 to 20 : 11 : 36, with a 2 - 200 kev luminosity of
+    3. 4 $ - $ 7. 27 $ \ times10 ^ { 38 } $ er'
+  - nuclei
+- source_sentence: What instruments used in guidance systems to indicate directions
+    in space must have an angular momentum that does not change in direction?
+  sentences:
+  - magnetic and transport properties of near - stoichiometric metastable fexmnygaz
+    alloys ( 46 < x < 52, 17 < y25, 26 < z < 30 ) with face - centered cubic ( fcc
+    ), body - centered cubic ( bcc ), and two - phase ( fcc + bcc ) structures are
+    investigated. the experimental results are analyzed in terms of first - principles
+    calculations of stoichiometric fe2mnga alloy with the l21, l12, and the tetragonally
+    distorted l21 structural orderings. it is shown that the pure bcc and fcc phases
+    have distinct magnetic
+  - k nearest neighbor ( knn ) joins are used in scientific domains for data analysis,
+    and are building blocks of several well - known algorithms. knn - joins find the
+    knn of all points in a dataset. this paper focuses on a hybrid cpu / gpu approach
+    for low - dimensional knn - joins, where the gpu may not yield substantial performance
+    gains over parallel cpu algorithms. we utilize a work queue that prioritizes computing
+    data points in high density regions on the gpu, and low density regions on the
+    cpu, thereby taking advantage of each architecture ' s relative strengths. our
+    approach, hybridknn - join,
+  - the fact that these states are effectively decoupled from propagating photons.
+    we prove that scattering of a parity - invariant single photon on a qubit pair,
+    combined with a properly engineered time variation of the qubit detuning, is not
+    only feasible, but also more effective than strategies based on the relaxation
+    of the excited states of the qubits. the use of tensor network methods to simulate
+    the proposed scheme enables to include photon delays in collision models, thus
+    opening the possibility to follow the time evolution of the full quantum system,
+    including qubits and field, and to efficiently implement and characterize the
+    dynamics in non
+- source_sentence: If pollination and fertilization occur, a diploid zygote forms
+    within an ovule, located where
+  sentences:
+  - while cosmic rays $ ( e \ gtrsim 1 \, \ mathrm { gev } ) $ are well coupled to
+    a galaxy ' s interstellar medium ( ism ) at scales of $ l > 100 \, \ mathrm {
+    pc } $, adjusting stratification and driving outflows, their impact on small scales
+    is less clear. based on calculations of the cosmic ray diffusion coefficient from
+    observations of the grammage in the milky way, cosmic rays have little time to
+    dynamically impact the ism on those small scales. using numerical simulations,
+    we explore how more complex cosmic ray transport could allow cosmic rays to couple
+  - centripetal force
+  - derived simple analytical expressions for the maximum growth rate, corresponding
+    to the most unstable mode of the system. these expressions provide the explicit
+    dependence of the growth rate on the various equilibrium parameters. for small
+    angles the growth time is linearly proportional to the shear angle, and in this
+    regime the single interface problem and the slab problem tend to the same result.
+    on the contrary, in the limit of large angles and for the interface problem the
+    growth time is essentially independent of the shear angle. in this regime we have
+    also been able to calculate an approximate expression for the growth time for
+    the slab configuration. magnetic shear can have a strong effect on the growth
+    rates
+- source_sentence: When the hydrogen is nearly used up, the star can fuse which element
+    into heavier elements?
+  sentences:
+  - the 50 ~ kton iron calorimeter ( ical ) detector at the underground india based
+    neutrino observatory ( ino ) will make measurements on atmospheric neutrinos.
+    muons produced in charged current ( cc ) interactions of muon neutrinos with the
+    iron are tracked spatially and temporally through the signals that they produce
+    in the resistive plate chambers ~ ( rpcs ) that are interleaved with iron layers.
+    since the rpcs will be operated in the avalanche mode the signal rise - time is
+    $ \ sim ~ 1 ~ \ rm { nsec } $ resulting in a fast time response
+  - magnesium in air
+  - pbhs. after discussing pbh formation as well as several inflation models leading
+    to pbh production, we summarize various existing and future observational constraints.
+    we then present topics on formation of pbh binaries, gravitational waves from
+    pbh binaries, various observational tests of pbhs by using gravitational waves.
+- source_sentence: How many different main types of diabetes are there?
+  sentences:
+  - skin
+  - two
+  - a connection between relativistic quantum mechanics in the foldy - wouthuysen
+    representation and the paraxial equations is established for a dirac particle
+    in external fields. the paraxial form of the landau eigenfunction for a relativistic
+    electron in a uniform magnetic field is determined. the obtained wave function
+    contains the gouy phase and significantly approaches to the paraxial wave function
+    for a free electron.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: sciq eval
+      type: sciq-eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.084
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.192
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.26
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.367
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.084
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.064
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.052
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0367
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.084
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.192
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.26
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.367
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.20773622543165599
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1588857142857143
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.17411230071844377
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'How many different main types of diabetes are there?',
+    'two',
+    'a connection between relativistic quantum mechanics in the foldy - wouthuysen representation and the paraxial equations is established for a dirac particle in external fields. the paraxial form of the landau eigenfunction for a relativistic electron in a uniform magnetic field is determined. the obtained wave function contains the gouy phase and significantly approaches to the paraxial wave function for a free electron.',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `sciq-eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.084      |
+| cosine_accuracy@3   | 0.192      |
+| cosine_accuracy@5   | 0.26       |
+| cosine_accuracy@10  | 0.367      |
+| cosine_precision@1  | 0.084      |
+| cosine_precision@3  | 0.064      |
+| cosine_precision@5  | 0.052      |
+| cosine_precision@10 | 0.0367     |
+| cosine_recall@1     | 0.084      |
+| cosine_recall@3     | 0.192      |
+| cosine_recall@5     | 0.26       |
+| cosine_recall@10    | 0.367      |
+| **cosine_ndcg@10**  | **0.2077** |
+| cosine_mrr@10       | 0.1589     |
+| cosine_map@100      | 0.1741     |
+
+<!--
+## 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: 46,716 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         | label                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | float                                                          |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 17.62 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 92.89 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.23</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | label            |
+  |:--------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Unlike plants, animal species rely almost exclusively on what type of reproduction?</code>                          | <code>0. 4, \ sim 4, and \ sim 300 \ mum are stronger than 10 ^ 5, 10 ^ 8, and 10 ^ 4 times those of the local interstellar radiation field ( isrf ). below these values, the chemical pumping is the dominant source of excitation of the j > 1 levels, even at high kinetic temperatures ( \ sim 1000 k ). the far - infrared emission lines of ch + observed in the orion bar and the ngc 7027 pdrs are consistent with the predictions of our excitation model assuming an incident far - ultraviolet ( fuv ) radiation field of \ sim 3 \ times 10</code>                                                                                                          | <code>0.0</code> |
+  | <code>What type of energy occurs by splitting the nuclei of radioactive uranium?</code>                                   | <code>we study the potential of future electron - ion collider ( eic ) data to probe four - fermion operators in the standard model effective field theory ( smeft ). the ability to perform measurements with both polarized electron and proton beams at the eic provides a powerful tool that can disentangle the effects from different smeft operators. we compare the potential constraints from an eic with those obtained from drell - yan data at the large hadron collider. we show that eic data plays an important complementary role since it probes combinations of wilson coefficients not accessible through available drell - yan measurements.</code> | <code>0.0</code> |
+  | <code>What element, which often forms polymers, has a unique ability to form covalent bonds with many other atoms?</code> | <code>some divergent series $ f $. the convergence sets on $ \ gamma : = \ { [ 1 : z : \ psi ( z ) ] : z \ in \ mathbb { c } \ } \ subset \ mathbb { c } ^ 2 \ subset \ mathbb { p } ^ 2 $, where $ \ psi $ is a transcendental entire holomorphic function, are also studied and we obtain that a subset on $ \ gamma $ is a convergence set in $ \ mathbb { p } ^ 2 $ if and only if it is a countable union of compact projectively convex sets, and</code>                                                                                                                                                                                                          | <code>0.0</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`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 1
+- `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`: 1
+- `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`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `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
+- `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
+| Epoch  | Step | Training Loss | sciq-eval_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:------------------------:|
+| 0.0685 | 100  | -             | 0.1200                   |
+| 0.1370 | 200  | -             | 0.1562                   |
+| 0.2055 | 300  | -             | 0.1780                   |
+| 0.2740 | 400  | -             | 0.1811                   |
+| 0.3425 | 500  | 3.1705        | 0.1909                   |
+| 0.4110 | 600  | -             | 0.1904                   |
+| 0.4795 | 700  | -             | 0.1955                   |
+| 0.5479 | 800  | -             | 0.2031                   |
+| 0.6164 | 900  | -             | 0.2014                   |
+| 0.6849 | 1000 | 2.9054        | 0.2002                   |
+| 0.7534 | 1100 | -             | 0.2058                   |
+| 0.8219 | 1200 | -             | 0.2083                   |
+| 0.8904 | 1300 | -             | 0.2084                   |
+| 0.9589 | 1400 | -             | 0.2076                   |
+| 1.0    | 1460 | -             | 0.2077                   |
+
+
+### Framework Versions
+- Python: 3.12.8
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- 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 +1,25 @@
+{
+  "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": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/Information-Retrieval_evaluation_sciq-eval_results.csv

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model.safetensors

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+oid sha256:e8aa1acedb6b15468abbbd574ccceaa9a3c4cc579752c2412433c9ae596b4ffa
+size 90864192

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
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+      "normalized": false,
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+      "single_word": false,
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+      "single_word": false,
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+  "cls_token": "[CLS]",
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+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
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+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
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+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}