Update README.md

README.md

@@ -1,526 +1,503 @@
----
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- dense
-- generated_from_trainer
-- dataset_size:67416
-- loss:MultipleNegativesRankingLoss
-widget:
-- source_sentence: ', k on their diagonal and zero elsewhere.'
-  sentences:
-  - 'The sample ACF and PACF of the data for that time period in Figure 6.17
-
-
-    466
-
-    TRANSFER FUNCTIONS AND INTERVENTION MODELS
-
-    100
-
-    80
-
-    60
-
-    Week
-
-    Week 88
-
-    40
-
-    20
-
-    0
-
-    100,000
-
-    160,000
-
-    140,000
-
-    Sales
-
-    200,000
-
-    180,000
-
-    220,000
-
-    120,000
-
-    FIGURE 6.16
-
-    Time series plot of the weekly sales data.'
-  - 'Just as in equation 6.10, we can write
-
-    X = u1a1vT
-
-    1 + u2a2vT
-
-    2 + · · · + ukakvT
-
-    k
-
-    (6.29)
-
-    We can ignore the corresponding ui, vi of very small, though nonzero,
-
-    ai and can still reconstruct X without too much error.'
-  - '13.8
-
-    Multiple Kernel Learning
-
-    It is possible to construct new kernels by combining simpler kernels.'
-- source_sentence: 'The main difference is that a node appears at most once as a neighbor
-    of an-
-
-    other node, whereas a word might appear more than once in the context of another
-    word.'
-  sentences:
-  - '3.2.6
-
-    A Decoupled View of Vector-Centric Backpropagation
-
-    In the previous discussion, two equivalent ways of computing the updates based
-    on Equa-
-
-    tions 3.12 and 3.18 were provided.'
-  - '(4.59),
-
-    we obtain
-
-    eT −(1 −𝜆)eT−1 = (yT −̂yT−1) −(1 −𝜆)(yT−1 −̂yT−2)
-
-    = yT −yT−1 −̂yT−1 + 𝜆yT−1 + (1 −𝜆)̂yT−2
-
-    ⏟⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏟⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏟
-
-    =̂yT−1
-
-    = yT −yT−1 −̂yT−1 + ̂yT−1
-
-    = yT −yT−1.'
-  - 8This fact is not evident in the toy example of Figure 2.17.
-- source_sentence: 'This influence is specified by the conditional probability
-
-    P(Y|X).'
-  sentences:
-  - 'Seasonality
-
-    is the component of time series behavior that repeats on a regular basis,
-
-    such as each year.'
-  - 'Note that one of the
-
-    classes is defined by strongly non-zero values in the first and third dimensions,
-    whereas the
-
-    second class is defined by strongly non-zero values in the second and fourth dimensions.'
-  - 'The nodes and the arcs between the nodes define the struc-
-
-    ture of the network, and the conditional probabilities are the parameters
-
-    given the structure.'
-- source_sentence: '238
-
-    9
-
-    Decision Trees
-
-    Rokach, L., and O. Maimon.'
-  sentences:
-  - '“Top-Down Induction of Decision Trees
-
-    Classifiers—A Survey.” IEEE Transactions on Systems, Man, and Cybernetics–
-
-    Part C 35:476–487.'
-  - 'The only feedback is at the
-
-    end of the game when we win or lose the game.'
-  - 'Subsequently, this computation is propagated
-
-    in the backwards direction with dynamic programming updates (similar to Equation
-    3.8).'
-- source_sentence: 'Therefore, one can use L1-regularization
-
-    to estimate which features are predictive to the application at hand.'
-  sentences:
-  - Blumer, A., A. Ehrenfeucht, D. Haussler, and M. K. Warmuth.
-  - What about the connections in the hidden layers whose weights are set to 0?
-  - 'In cases where computational complexity is important, such
-
-    as in a production setting where thousands of models are being fit, it may not
-    be
-
-    worth the extra computational effort.'
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
-metrics:
-- pearson_cosine
-- spearman_cosine
-model-index:
-- name: SentenceTransformer
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: val
-      type: val
-    metrics:
-    - type: pearson_cosine
-      value: .nan
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: .nan
-      name: Spearman Cosine
----
-
-# SentenceTransformer
-
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 384 tokens
-- **Output Dimensionality:** 768 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'Therefore, one can use L1-regularization\nto estimate which features are predictive to the application at hand.',
-    'What about the connections in the hidden layers whose weights are set to 0?',
-    'In cases where computational complexity is important, such\nas in a production setting where thousands of models are being fit, it may not be\nworth the extra computational effort.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities)
-# tensor([[ 1.0000,  0.4198,  0.2089],
-#         [ 0.4198,  1.0000, -0.0369],
-#         [ 0.2089, -0.0369,  1.0000]])
-```
-
-<!--
-### 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
-
-#### Semantic Similarity
-
-* Dataset: `val`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value   |
-|:--------------------|:--------|
-| pearson_cosine      | nan     |
-| **spearman_cosine** | **nan** |
-
-<!--
-## 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: 67,416 training samples
-* Columns: <code>sentence_0</code> and <code>sentence_1</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                         | sentence_1                                                                         |
-  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                             |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 39.68 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.93 tokens</li><li>max: 384 tokens</li></ul> |
-* Samples:
-  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | sentence_1                                                                                                                                                                                                                                          |
-  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Leveraging Redundancies in Weights<br>It was shown in [94] that the vast majority of the weights in a neural network are redundant.</code>                                                                                                                                                                                                                                                                                                                                                                        | <code>Furthermore, it is assumed that k ≪min{m1, m2}.</code>                                                                                                                                                                                        |
-  | <code>Aran, O., O. T. Yıldız, and E. Alpaydın.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>“An Incremental Framework Based<br>on Cross-Validation for Estimating the Architecture of a Multilayer Percep-<br>tron.” International Journal of Pattern Recognition and Artificial Intelligence<br>23:159–190.</code>                        |
-  | <code>(a)<br>(d)<br>(e)<br>(f)<br><br>29<br>Code is life<br>input_decoder = Input(shape=(latent_dim,), name="decoder_input")   <br>decoder_h = Dense(intermediate_dim, activation='relu',           <br>name="decoder_h")(input_decoder)<br>x_decoded = Dense(original_dim, activation='sigmoid', <br>name="flat_decoded")(decoder_h)                                 <br>decoder = Model(input_decoder, x_decoded, name="decoder")  <br>We can now combine the encoder and the decoder into a single VAE model.</code> | <code>output_combined = decoder(encoder(x)[2])      <br>vae = Model(x, output_combined)      <br>vae.summary()    <br>Next, we get to the more familiar parts of machine learning: defining a loss function<br>so our autoencoder can train.</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",
-      "gather_across_devices": false
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 6
-- `fp16`: True
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 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`: 6
-- `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
-- `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}
-- `parallelism_config`: None
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `project`: huggingface
-- `trackio_space_id`: trackio
-- `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
-- `hub_revision`: None
-- `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`: no
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `use_liger_kernel`: False
-- `liger_kernel_config`: None
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: True
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-- `router_mapping`: {}
-- `learning_rate_mapping`: {}
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss | val_spearman_cosine |
-|:------:|:----:|:-------------:|:-------------------:|
-| 0.1187 | 500  | 1.5671        | -                   |
-| 0.2373 | 1000 | 1.2804        | -                   |
-| 0.3560 | 1500 | 1.1256        | -                   |
-| 0.4746 | 2000 | 0.9789        | -                   |
-| 0.5933 | 2500 | 0.8839        | -                   |
-| 0.7119 | 3000 | 0.7748        | -                   |
-| 0.8306 | 3500 | 0.73          | -                   |
-| 0.9492 | 4000 | 0.698         | -                   |
-| 1.0    | 4214 | -             | nan                 |
-
-
-### Framework Versions
-- Python: 3.11.7
-- Sentence Transformers: 5.1.1
-- Transformers: 4.57.0
-- PyTorch: 2.5.1+cu121
-- Accelerate: 1.12.0
-- Datasets: 4.4.1
-- Tokenizers: 0.22.1
-
-## 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.*
+---
+tags:
+- Data-Science
+- Machine-Learning
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:67416
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: ', k on their diagonal and zero elsewhere.'
+  sentences:
+  - |-
+    The sample ACF and PACF of the data for that time period in Figure 6.17
+
+    466
+    TRANSFER FUNCTIONS AND INTERVENTION MODELS
+    100
+    80
+    60
+    Week
+    Week 88
+    40
+    20
+    0
+    100,000
+    160,000
+    140,000
+    Sales
+    200,000
+    180,000
+    220,000
+    120,000
+    FIGURE 6.16
+    Time series plot of the weekly sales data.
+  - |-
+    Just as in equation 6.10, we can write
+    X = u1a1vT
+    1 + u2a2vT
+    2 + · · · + ukakvT
+    k
+    (6.29)
+    We can ignore the corresponding ui, vi of very small, though nonzero,
+    ai and can still reconstruct X without too much error.
+  - |-
+    13.8
+    Multiple Kernel Learning
+    It is possible to construct new kernels by combining simpler kernels.
+- source_sentence: >-
+    The main difference is that a node appears at most once as a neighbor of an-
+
+    other node, whereas a word might appear more than once in the context of
+    another word.
+  sentences:
+  - >-
+    3.2.6
+
+    A Decoupled View of Vector-Centric Backpropagation
+
+    In the previous discussion, two equivalent ways of computing the updates
+    based on Equa-
+
+    tions 3.12 and 3.18 were provided.
+  - |-
+    (4.59),
+    we obtain
+    eT −(1 −𝜆)eT−1 = (yT −̂yT−1) −(1 −𝜆)(yT−1 −̂yT−2)
+    = yT −yT−1 −̂yT−1 + 𝜆yT−1 + (1 −𝜆)̂yT−2
+    ⏟⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏟⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏟
+    =̂yT−1
+    = yT −yT−1 −̂yT−1 + ̂yT−1
+    = yT −yT−1.
+  - 8This fact is not evident in the toy example of Figure 2.17.
+- source_sentence: |-
+    This influence is specified by the conditional probability
+    P(Y|X).
+  sentences:
+  - |-
+    Seasonality
+    is the component of time series behavior that repeats on a regular basis,
+    such as each year.
+  - >-
+    Note that one of the
+
+    classes is defined by strongly non-zero values in the first and third
+    dimensions, whereas the
+
+    second class is defined by strongly non-zero values in the second and fourth
+    dimensions.
+  - |-
+    The nodes and the arcs between the nodes define the struc-
+    ture of the network, and the conditional probabilities are the parameters
+    given the structure.
+- source_sentence: |-
+    238
+    9
+    Decision Trees
+    Rokach, L., and O. Maimon.
+  sentences:
+  - |-
+    “Top-Down Induction of Decision Trees
+    Classifiers—A Survey.” IEEE Transactions on Systems, Man, and Cybernetics–
+    Part C 35:476–487.
+  - |-
+    The only feedback is at the
+    end of the game when we win or lose the game.
+  - >-
+    Subsequently, this computation is propagated
+
+    in the backwards direction with dynamic programming updates (similar to
+    Equation 3.8).
+- source_sentence: |-
+    Therefore, one can use L1-regularization
+    to estimate which features are predictive to the application at hand.
+  sentences:
+  - Blumer, A., A. Ehrenfeucht, D. Haussler, and M. K. Warmuth.
+  - What about the connections in the hidden layers whose weights are set to 0?
+  - >-
+    In cases where computational complexity is important, such
+
+    as in a production setting where thousands of models are being fit, it may
+    not be
+
+    worth the extra computational effort.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: val
+      type: val
+    metrics:
+    - type: pearson_cosine
+      value: null
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: null
+      name: Spearman Cosine
+license: apache-2.0
+language:
+- en
+base_model:
+- sentence-transformers/all-mpnet-base-v2
+datasets:
+- DigitalAsocial/ds-tb-5-raw
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Therefore, one can use L1-regularization\nto estimate which features are predictive to the application at hand.',
+    'What about the connections in the hidden layers whose weights are set to 0?',
+    'In cases where computational complexity is important, such\nas in a production setting where thousands of models are being fit, it may not be\nworth the extra computational effort.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000,  0.4198,  0.2089],
+#         [ 0.4198,  1.0000, -0.0369],
+#         [ 0.2089, -0.0369,  1.0000]])
+```
+
+<!--
+### 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
+
+#### Semantic Similarity
+
+* Dataset: `val`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value   |
+|:--------------------|:--------|
+| pearson_cosine      | nan     |
+| **spearman_cosine** | **nan** |
+
+<!--
+## 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: 67,416 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 39.68 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.93 tokens</li><li>max: 384 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | sentence_1                                                                                                                                                                                                                                          |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Leveraging Redundancies in Weights<br>It was shown in [94] that the vast majority of the weights in a neural network are redundant.</code>                                                                                                                                                                                                                                                                                                                                                                        | <code>Furthermore, it is assumed that k ≪min{m1, m2}.</code>                                                                                                                                                                                        |
+  | <code>Aran, O., O. T. Yıldız, and E. Alpaydın.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>“An Incremental Framework Based<br>on Cross-Validation for Estimating the Architecture of a Multilayer Percep-<br>tron.” International Journal of Pattern Recognition and Artificial Intelligence<br>23:159–190.</code>                        |
+  | <code>(a)<br>(d)<br>(e)<br>(f)<br><br>29<br>Code is life<br>input_decoder = Input(shape=(latent_dim,), name="decoder_input")   <br>decoder_h = Dense(intermediate_dim, activation='relu',           <br>name="decoder_h")(input_decoder)<br>x_decoded = Dense(original_dim, activation='sigmoid', <br>name="flat_decoded")(decoder_h)                                 <br>decoder = Model(input_decoder, x_decoded, name="decoder")  <br>We can now combine the encoder and the decoder into a single VAE model.</code> | <code>output_combined = decoder(encoder(x)[2])      <br>vae = Model(x, output_combined)      <br>vae.summary()    <br>Next, we get to the more familiar parts of machine learning: defining a loss function<br>so our autoencoder can train.</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",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 6
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 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`: 6
+- `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
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `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
+- `hub_revision`: None
+- `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`: no
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: True
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | val_spearman_cosine |
+|:------:|:----:|:-------------:|:-------------------:|
+| 0.1187 | 500  | 1.5671        | -                   |
+| 0.2373 | 1000 | 1.2804        | -                   |
+| 0.3560 | 1500 | 1.1256        | -                   |
+| 0.4746 | 2000 | 0.9789        | -                   |
+| 0.5933 | 2500 | 0.8839        | -                   |
+| 0.7119 | 3000 | 0.7748        | -                   |
+| 0.8306 | 3500 | 0.73          | -                   |
+| 0.9492 | 4000 | 0.698         | -                   |
+| 1.0    | 4214 | -             | nan                 |
+
+
+### Framework Versions
+- Python: 3.11.7
+- Sentence Transformers: 5.1.1
+- Transformers: 4.57.0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.12.0
+- Datasets: 4.4.1
+- Tokenizers: 0.22.1
+
+## 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.*
 -->