Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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:475
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: 'We will analyze the $K$-means algorithm and show that it always
+    converge. Let us consider the $K$-means objective function: $$ \mathcal{L}(\mathbf{z},
+    \boldsymbol{\mu})=\sum_{n=1}^{N} \sum_{k=1}^{K} z_{n k}\left\|\mathbf{x}_{n}-\boldsymbol{\mu}_{k}\right\|_{2}^{2}
+    $$ where $z_{n k} \in\{0,1\}$ with $\sum_{k=1}^{K} z_{n k}=1$ and $\boldsymbol{\mu}_{k}
+    \in \mathbb{R}^{D}$ for $k=1, \ldots, K$ and $n=1, \ldots, N$. How would you choose
+    $\left\{\boldsymbol{\mu}_{k}\right\}_{k=1}^{K}$ to minimize $\mathcal{L}(\mathbf{z},
+    \boldsymbol{\mu})$ for given $\left\{z_{n k}\right\}_{n, k=1}^{N, K}$ ? Compute
+    the closed-form formula for the $\boldsymbol{\mu}_{k}$. To which step of the $K$-means
+    algorithm does it correspond?'
+  sentences:
+  - "1. Dynamically scheduled processors have universally more\n        physical registers\
+    \ than the typical 32 architectural ones and\n        they are used for removing\
+    \ WARs and WAW (name\n        dependencies). In VLIW processors, the same renaming\
+    \ must be\n        done by the compiler and all registers must be architecturally\n\
+    \        visible.\n        2. Also, various techniques essential to improve the\n\
+    \        performance of VLIW processors consume more registers (e.g.,\n      \
+    \  loop unrolling or loop fusion). "
+  - O( (f+1)n^2 )b in the binary case, or O( (f+1)n^3 )b in the non-binary case
+  - The idea is wrong. Even if the interface remains the same since we are dealing
+    with character strings, a decorator does not make sense because the class returning
+    JSON cannot be used without this decorator; the logic for extracting the weather
+    prediction naturally belongs to the weather client in question. It is therefore
+    better to create a class containing both the download of the JSON and the extraction
+    of the weather forecast.
+- source_sentence: Estimate the 95% confidence intervals of the geometric mean and
+    the arithmetic mean of pageviews using bootstrap resampling. The data is given
+    in a pandas.DataFrame called df and the respective column is called "pageviews".
+    You can use the scipy.stats python library.
+  sentences:
+  - (a) PoS tagging, but also Information Retrieval (IR), Text Classification, Information
+    Extraction. For the later, accuracy sounds like precision (but it depends on what
+    we actually mean by 'task' (vs. subtask)) . (b) a reference must be available,
+    'correct' and 'incorrect' must be clearly defined
+  - '[[''break+V => breakable\xa0'', ''derivational''], [''freeze+V =>\xa0frozen\xa0'',
+    ''inflectional''], [''translate+V => translation'', ''derivational''], [''cat+N
+    => cats'', ''inflectional''], [''modify+V => modifies '', ''inflectional'']]'
+  - Dynamically scheduled out-of-order processors.
+- source_sentence: "The data contains information about submissions to a prestigious\
+    \ machine learning conference called ICLR. Columns:\nyear, paper, authors, ratings,\
+    \ decisions, institution, csranking, categories, authors_citations, authors_publications,\
+    \ authors_hindex, arxiv. The data is stored in a pandas.DataFrame format. \n\n\
+    Create 3 new fields in the dataframe corresponding to the median value of the\
+    \ number of citations per author, the number of publications per author, and the\
+    \ h-index per author. So for instance, for the row authors_publications, you will\
+    \ create an additional column, e.g. authors_publications_median, containing the\
+    \ median number of publications per author in each paper."
+  sentences:
+  - Consider an deterministic online algorithm $\Alg$ and set $x_1 = W$. There are
+    two cases depending on whether \Alg trades the $1$ Euro the  first day or not.  Suppose
+    first that $\Alg$ trades the Euro at day $1$. Then we set $x_2 = W^2$ and so the
+    algorithm is only $W/W^2 = 1/W$ competitive. For the other case when  \Alg waits
+    for the second day,  we set $x_2 = 1$. Then \Alg gets $1$ Swiss franc whereas
+    optimum would get $W$ and so the algorithm is only $1/W$ competitive again.
+  - 'This is an abstraction leak: the notion of JavaScript and even a browser is a
+    completely different level of abstraction than users, so this method will likely
+    lead to bugs.'
+  - 'We could consider at least two approaches here: either binomial confidence interval
+    or t-test. • binomial confidence interval: evaluation of a binary classifier (success
+    or not) follow a binomial law with parameters (perror,T), where T is the test-set
+    size (157 in the above question; is it big enough?). Using normal approximation
+    of the binomial law, the width of the confidence interval around estimated error
+    probability is q(α)*sqrt(pb*(1-pb)/T),where q(α) is the 1-α quantile (for a 1
+    - α confidence level) and pb is the estimation of perror. We here want this confidence
+    interval width to be 0.02, and have pb = 0.118 (and ''know'' that q(0.05) = 1.96
+    from normal distribution quantile charts); thus we have to solve: (0.02)^2 = (1.96)^2*(0.118*(1-0.118))/T
+    Thus T ≃ 1000. • t-test approach: let''s consider estimating their relative behaviour
+    on each of the test cases (i.e. each test estimation subset is of size 1). If
+    the new system as an error of 0.098 (= 0.118 - 0.02), it can vary from system
+    3 between 0.02 of the test cases (both systems almost always agree but where the
+    new system improves the results) and 0.216 of the test cases (the two systems
+    never make their errors on the same test case, so they disagree on 0.118 + 0.098
+    of the cases). Thus μ of the t-test is between 0.02 and 0.216. And s = 0.004 (by
+    assumption, same variance). Thus t is between 5*sqrt(T) and 54*sqrt(T) which is
+    already bigger than 1.645 for any T bigger than 1. So this doesn''t help much.
+    So all we can say is that if we want to have a (lowest possible) difference of
+    0.02 we should have at least 1/0.02 = 50 test cases ;-) And if we consider that
+    we have 0.216 difference, then we have at least 5 test cases... The reason why
+    these numbers are so low is simply because we here make strong assumptions about
+    the test setup: that it is a paired evaluation. In such a case, having a difference
+    (0.02) that is 5 times bigger than the standard deviation is always statistically
+    significant at a 95% level.'
+- source_sentence: In order to summarize the degree distribution in a single number,
+    would you recommend using the average degree? Why, or why not? If not, what alternatives
+    can you think of? Please elaborate!
+  sentences:
+  - 'inflectional morphology: no change in the grammatical category (e.g. give, given,
+    gave, gives ) derivational morphology: change in category (e.g. process, processing,
+    processable, processor, processabilty)'
+  - "$ \text{Var}[\\wv^\top \\xx] = \frac1N \\sum_{n=1}^N (\\wv^\top \\xx_n)^2$ %\n"
+  - '- $E(X) = 0.5$, $Var(X) = 1/12$
+
+    - $E(Y) = 0.5$, $Var(Y) = 1/12$
+
+    - $E(Z) = 0.6$, $Var(Z) = 1/24$
+
+    - $E(K) = 0.6$, $Var(K) = 1/12$'
+- source_sentence: ' The [t-statistic](https://en.wikipedia.org/wiki/T-statistic)
+    is the ratio of the departure of the estimated value of a parameter from its hypothesized
+    value to its standard error. In a t-test, the higher the t-statistic, the more
+    confidently we can reject the null hypothesis. Use `numpy.random` to create four
+    samples, each of size 30:
+
+    - $X \sim Uniform(0,1)$
+
+    - $Y \sim Uniform(0,1)$
+
+    - $Z = X/2 + Y/2 + 0.1$
+
+    - $K = Y + 0.1$'
+  sentences:
+  - 'The simplest solution to produce the indexing set associated with a document
+    is to use a stemmer associated with stop lists allowing to ignore specific non
+    content bearing terms. In this case, the indexing set associated with D might
+    be:
+
+
+    $I(D)=\{2006$, export, increas, Switzerland, USA $\}$
+
+
+    A more sophisticated approach would consist in using a lemmatizer in which case,
+    the indexing set might be:
+
+
+    $I(D)=\left\{2006 \_N U M\right.$, export\_Noun, increase\_Verb, Switzerland\_ProperNoun,
+    USA\_ProperNoun\}'
+  - Including a major bugfix in a minor release instead of a bugfix release will cause
+    an incoherent changelog and an inconvenience for users who wish to only apply
+    the patch without any other changes. The bugfix could be as well an urgent security
+    fix and should not wait to the next minor release date.
+  - 'def get_vocabulary_frequency(documents):     """     It parses the input documents
+    and creates a dictionary with the terms and term frequencies.          INPUT:     Doc1:
+    hello hello world     Doc2: hello friend          OUTPUT:     {''hello'': 3,     ''world'':
+    1,     ''friend'': 1}      :param documents: list of list of str, with the tokenized
+    documents.     :return: dict, with keys the words and values the frequency of
+    each word.     """     vocabulary = dict()          for document in documents:         for
+    word in document:             if word in vocabulary:                 vocabulary[word]
+    += 1             else:                 vocabulary[word] = 1      return vocabulary'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# 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("AShi846/fine-tuned-embedding-model")
+# Run inference
+sentences = [
+    ' The [t-statistic](https://en.wikipedia.org/wiki/T-statistic) is the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. In a t-test, the higher the t-statistic, the more confidently we can reject the null hypothesis. Use `numpy.random` to create four samples, each of size 30:\n- $X \\sim Uniform(0,1)$\n- $Y \\sim Uniform(0,1)$\n- $Z = X/2 + Y/2 + 0.1$\n- $K = Y + 0.1$',
+    'def get_vocabulary_frequency(documents):     """     It parses the input documents and creates a dictionary with the terms and term frequencies.          INPUT:     Doc1: hello hello world     Doc2: hello friend          OUTPUT:     {\'hello\': 3,     \'world\': 1,     \'friend\': 1}      :param documents: list of list of str, with the tokenized documents.     :return: dict, with keys the words and values the frequency of each word.     """     vocabulary = dict()          for document in documents:         for word in document:             if word in vocabulary:                 vocabulary[word] += 1             else:                 vocabulary[word] = 1      return vocabulary',
+    'Including a major bugfix in a minor release instead of a bugfix release will cause an incoherent changelog and an inconvenience for users who wish to only apply the patch without any other changes. The bugfix could be as well an urgent security fix and should not wait to the next minor release date.',
+]
+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: 475 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 475 samples:
+  |         | sentence_0                                                                          | sentence_1                                                                         | label                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             | float                                                         |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 135.81 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 110.0 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.1</li><li>max: 0.1</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | label            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>You have just started your prestigious and important job as the Swiss Cheese Minister. As it turns out, different fondues and raclettes have different nutritional values and different prices: \begin{center} \begin{tabular}{|l|l|l|l||l|} \hline Food & Fondue moitie moitie & Fondue a la tomate & Raclette & Requirement per week \\ \hline Vitamin A [mg/kg] & 35 & 0.5 & 0.5 & 0.5 mg   \\ Vitamin B [mg/kg] & 60 & 300 & 0.5 & 15 mg   \\ Vitamin C [mg/kg] & 30 & 20 & 70 & 4 mg   \\ \hline [price [CHF/kg] & 50 & 75 & 60 & --- \\ \hline \end{tabular} \end{center} Formulate the problem of finding the cheapest combination of the different fondues (moitie moitie \& a la tomate) and Raclette so as to satisfy the weekly nutritional requirement  as a  linear program.</code>                                                                                                                                                                                                       | <code>1. The adjacency graph has ones everywhere except for (i) no<br>                    edges between 	exttt{sum} and 	exttt{i}, and between<br>                    	exttt{sum} and 	exttt{y\_coord}, and (ii) five on the edge<br>                    between 	exttt{x\_coord} and 	exttt{y\_coord}, and two on the<br>                    edge between 	exttt{i} and 	exttt{y\_coord}.<br>                    2. Any of these solution should be optimal either as shown or<br>                    reversed:<br>                    <br>                    - 	exttt{x\_coord}, 	exttt{y\_coord}, 	exttt{i},<br>                        	exttt{j}, 	exttt{sum}<br>                    - 	exttt{j}, 	exttt{sum}, 	exttt{x\_coord},<br>                        	exttt{y\_coord}, 	exttt{i}<br>                    - 	exttt{sum}, 	exttt{j}, 	exttt{x\_coord},<br>                        	exttt{y\_coord}, 	exttt{i}<br>                    3. Surely, this triad should be adjacent: 	exttt{x\_coord}, 	exttt{y\_coord}, 	exttt{i}.</code> | <code>0.1</code> |
+  | <code>Describe the techniques that typical dynamically scheduled<br>            processors use to achieve the same purpose of the following features<br>            of Intel Itanium: (a) Predicated execution; (b) advanced<br>            loads---that is, loads moved before a store and explicit check for<br>            RAW hazards; (c) speculative loads---that is, loads moved before a<br>            branch and explicit check for exceptions; (d) rotating register<br>            file.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | <code>Alice and Bob can both apply the AMS sketch with constant precision and failure probability $1/n^2$ to their vectors. Then Charlie subtracts the sketches from each other, obtaining a sketch of the difference. Once the sketch of the difference is available, one can find the special word similarly to the previous problem.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>0.1</code> |
+  | <code>Design and analyze a polynomial time algorithm for the following problem: \begin{description} \item[INPUT:] An undirected graph $G=(V,E)$. \item[OUTPUT:] A non-negative vertex potential $p(v)\geq 0$ for each vertex $v\in V$ such that \begin{align*} \sum_{v\in S} p(v) \leq |E(S, \bar S)| \quad \mbox{for every $\emptyset \neq S \subsetneq V$ \quad and \quad $\sum_{v\in V} p(v)$ is maximized.} \end{align*} \end{description} {\small (Recall that $E(S, \bar S)$ denotes the set of edges that cross the cut defined by $S$, i.e., $E(S, \bar S) = \{e\in E: |e\cap S| = |e\cap \bar S| = 1\}$.)} \\[1mm] \noindent Hint: formulate the problem as a large linear program (LP) and then show that the LP can be solved in polynomial time.  \\[1mm] {\em (In this problem you are asked to (i) design the algorithm, (ii) show that it returns a correct solution and that it runs in polynomial time.   Recall that you are allowed to refer to material covered in the course.) }</code> | <code>precision = tp/(tp+fp) recall = tp/(tp+fn) f_measure = 2*precision*recall/(precision+recall) print('F-measure: ', f_measure)</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>0.1</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 3
+- `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>
+
+### Framework Versions
+- Python: 3.12.8
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu126
+- 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",
+}
+```
+
+<!--
+## 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": "2.6.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3612e3c54dc40da7459a80e4371423d5f2d4c385c763813cfdf2d71c39bd773c
+size 90864192

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": 256,
+  "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 +1,65 @@
+{
+  "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": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "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]"
+}