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Add new SentenceTransformer model

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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1024,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: BAAI/bge-large-en-v1.5
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+ library_name: sentence-transformers
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:4370
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+ - loss:CosineSimilarityLoss
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+ widget:
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+ - source_sentence: '
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+
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+ Construct: Recognise a linear graph from its shape
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+
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+ Subject: Finding the Gradient and Intercept of a Line from the Equation
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+
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+ Question: Use a graphing program (e.g. Desmos) to plot the following pairs of
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+ functions.
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+
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+ \[
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+
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+ y=3 \text { and } y=-2
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+
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+ \]
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+
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+
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+ Tom says both functions are linear
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+
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+
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+ Katie says both functions are vertical lines
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+
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+
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+ Who is correct?
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+
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+ Incorrect Answer: Neither is correct
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+
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+ Correct Answer: Only
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+
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+ Tom
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+
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+ '
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+ sentences:
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+ - Believes the coefficent of x in an expanded quadratic comes from multiplying the
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+ two numbers in the brackets
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+ - Does not know the properties of a linear graph
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+ - Misremembers the quadratic formula
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+ - source_sentence: '
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+
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+ Construct: Multiply two decimals together with the same number of decimal places
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+
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+ Subject: Multiplying and Dividing with Decimals
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+
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+ Question: \( 0.6 \times 0.4= \)
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+
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+ Incorrect Answer: \( 2.4 \)
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+
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+ Correct Answer: \( 0.24 \)
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+
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+ '
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+ sentences:
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+ - When asked to solve simultaneous equations, believes they can just find values
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+ that work in one equation
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+ - Believes the solutions of a quadratic equation are the constants in the factorised
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+ form
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+ - When multiplying decimals, divides by the wrong power of 10 when reinserting the
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+ decimal
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+ - source_sentence: '
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+
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+ Construct: Estimate the volume or capacity of an object
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+
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+ Subject: Volume of Prisms
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+
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+ Question: Each of these measurements matches one of these objects. ![An image
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+ of 4 objects and 4 measurements. The objects are an egg cup, a cereal box, a chest
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+ of drawers and a piggy bank. And, the measurements are 87 cm^3, 1013 cm^3, 4172
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+ cm^3 and 197,177 cm^3.]() Which measurement most likely matches the egg cup?
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+
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+ Incorrect Answer: \( 197177 \mathrm{~cm}^{3} \)
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+
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+ Correct Answer: \( 87 \mathrm{~cm}^{3} \)
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+
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+ '
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+ sentences:
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+ - Confuses quadratic and exponential graphs
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+ - Cannot estimate the relative volume order, for different objects
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+ - Does not know how many days are in a leap year
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+ - source_sentence: '
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+
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+ Construct: Carry out division problems involving one negative integer
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+
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+ Subject: Multiplying and Dividing Negative Numbers
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+
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+ Question: \( 12 \div(-4)= \)
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+
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+ Incorrect Answer: \( 3 \)
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+
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+ Correct Answer: \( -3 \)
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+
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+ '
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+ sentences:
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+ - Believes dividing a positive by a negative gives a positive answer
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+ - Believes -a is always smaller than a, ignoring the possibility that a is negative
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+ - Subtracts instead of divides
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+ - source_sentence: '
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+
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+ Construct: Construct frequency tables
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+
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+ Subject: Frequency tables
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+
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+ Question: Dave has recorded the number of pets his classmates have in the frequency
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+ table on the right. \begin{tabular}{|c|c|}
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+
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+ \hline Number of pets & Frequency \\
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+
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+ \hline \( 0 \) & \( 4 \) \\
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+
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+ \hline \( 1 \) & \( 6 \) \\
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+
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+ \hline \( 2 \) & \( 3 \) \\
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+
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+ \hline \( 3 \) & \( 2 \) \\
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+
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+ \hline \( 4 \) & \( 5 \) \\
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+
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+ \hline
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+
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+ \end{tabular} If Dave wanted to work out the total number of pets own by his classmates,
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+ what would be a useful column to include?
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+
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+ Incorrect Answer: Number of pets -
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+
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+ Frequency
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+
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+ Correct Answer: Number of pets \( x \) Frequency
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+
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+ '
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+ sentences:
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+ - Subtracts rather than multiplies when calculating total frequency
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+ - Does not follow the arrows through a function machine, changes the order of the
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+ operations asked.
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+ - 'Believes the intersection in a prime factor venn diagram does not contribute
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+ to the size of the number represented by a circle '
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+ ---
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+
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+ # SentenceTransformer based on BAAI/bge-large-en-v1.5
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 1024 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ (2): Normalize()
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
184
+
185
+ ```bash
186
+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("VaggP/bge-fine-tuned")
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+ # Run inference
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+ sentences = [
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+ '\nConstruct: Construct frequency tables\nSubject: Frequency tables\nQuestion: Dave has recorded the number of pets his classmates have in the frequency table on the right. \\begin{tabular}{|c|c|}\n\\hline Number of pets & Frequency \\\\\n\\hline \\( 0 \\) & \\( 4 \\) \\\\\n\\hline \\( 1 \\) & \\( 6 \\) \\\\\n\\hline \\( 2 \\) & \\( 3 \\) \\\\\n\\hline \\( 3 \\) & \\( 2 \\) \\\\\n\\hline \\( 4 \\) & \\( 5 \\) \\\\\n\\hline\n\\end{tabular} If Dave wanted to work out the total number of pets own by his classmates, what would be a useful column to include?\nIncorrect Answer: Number of pets -\nFrequency\nCorrect Answer: Number of pets \\( x \\) Frequency\n',
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+ 'Subtracts rather than multiplies when calculating total frequency',
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+ 'Does not follow the arrows through a function machine, changes the order of the operations asked.',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 1024]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
214
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 4,370 training samples
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+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence_0 | sentence_1 | label |
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+ |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 38 tokens</li><li>mean: 98.75 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.91 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence_0 | sentence_1 | label |
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+ |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------|
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+ | <code><br>Construct: Construct a pictogram involving fractions of symbols<br>Subject: Pictogram<br>Question: This pictogram shows the different types of music Bob has in his music collection.<br>Bob has \( 2 \) rave CDs.<br><br>How would he display this on the pictogram? ![A pictogram showing the number of CDs Bob has in his musical collection. Pop has 3 and a half symbols, rock has 2 symbols, blues has 2 and a quarter symbols, jazz has 3 and a quarter symbols and classical has 1 and three-quarter symbols. Each symbol represents 4 CDs.]()<br>Incorrect Answer: ![\( 00 \)]()<br>Correct Answer: ![\( 0 \)]()<br></code> | <code>When interpreting a pictogram, thinks each symbol stands for 1</code> | <code>1.0</code> |
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+ | <code><br>Construct: Use brackets to write function machines as calculations<br>Subject: Writing Expressions<br>Question: Tom and Katie are arguing about the result of this Function Machine:<br>Tom says the output is: \( 3 n-12 \)<br>Katie says the output is: \( 3(n-4) \)<br>Who is correct? ![A function machine with input n and operations subtract 4, multiply by 3]()<br>Incorrect Answer: Only Tom<br>Correct Answer: Both Tom and Katie<br></code> | <code>Does not think a factorised expression is equivalent to its multiplied out form</code> | <code>1.0</code> |
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+ | <code><br>Construct: Interpret linear sections of real life graphs<br>Subject: Real Life Graphs<br>Question: The graph on the right shows the mass of sand in a bucket over time<br><br>What might the horizontal section represent? ![A graph with time (secs) on the horizontal axis and mass (g) on the vertical axis. The graph starts at the origin, travels in a straight line up and right, travels horizontally, then travels in a straight line down and right back to the x-axis, more steeply than the start. ]()<br>Incorrect Answer: Sand is being tipped out<br>Correct Answer: The bucket is full<br></code> | <code>Believes a horizontal line can show a constant rate of change</code> | <code>1.0</code> |
267
+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
268
+ ```json
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+ {
270
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
271
+ }
272
+ ```
273
+
274
+ ### Training Hyperparameters
275
+ #### Non-Default Hyperparameters
276
+
277
+ - `num_train_epochs`: 1
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+ - `multi_dataset_batch_sampler`: round_robin
279
+
280
+ #### All Hyperparameters
281
+ <details><summary>Click to expand</summary>
282
+
283
+ - `overwrite_output_dir`: False
284
+ - `do_predict`: False
285
+ - `eval_strategy`: no
286
+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 8
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+ - `per_device_eval_batch_size`: 8
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
291
+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
293
+ - `torch_empty_cache_steps`: None
294
+ - `learning_rate`: 5e-05
295
+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
299
+ - `max_grad_norm`: 1
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
304
+ - `warmup_ratio`: 0.0
305
+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
309
+ - `logging_nan_inf_filter`: True
310
+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: False
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
330
+ - `tpu_num_cores`: None
331
+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
334
+ - `dataloader_num_workers`: 0
335
+ - `dataloader_prefetch_factor`: None
336
+ - `past_index`: -1
337
+ - `disable_tqdm`: False
338
+ - `remove_unused_columns`: True
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+ - `label_names`: None
340
+ - `load_best_model_at_end`: False
341
+ - `ignore_data_skip`: False
342
+ - `fsdp`: []
343
+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
350
+ - `optim_args`: None
351
+ - `adafactor`: False
352
+ - `group_by_length`: False
353
+ - `length_column_name`: length
354
+ - `ddp_find_unused_parameters`: None
355
+ - `ddp_bucket_cap_mb`: None
356
+ - `ddp_broadcast_buffers`: False
357
+ - `dataloader_pin_memory`: True
358
+ - `dataloader_persistent_workers`: False
359
+ - `skip_memory_metrics`: True
360
+ - `use_legacy_prediction_loop`: False
361
+ - `push_to_hub`: False
362
+ - `resume_from_checkpoint`: None
363
+ - `hub_model_id`: None
364
+ - `hub_strategy`: every_save
365
+ - `hub_private_repo`: False
366
+ - `hub_always_push`: False
367
+ - `gradient_checkpointing`: False
368
+ - `gradient_checkpointing_kwargs`: None
369
+ - `include_inputs_for_metrics`: False
370
+ - `eval_do_concat_batches`: True
371
+ - `fp16_backend`: auto
372
+ - `push_to_hub_model_id`: None
373
+ - `push_to_hub_organization`: None
374
+ - `mp_parameters`:
375
+ - `auto_find_batch_size`: False
376
+ - `full_determinism`: False
377
+ - `torchdynamo`: None
378
+ - `ray_scope`: last
379
+ - `ddp_timeout`: 1800
380
+ - `torch_compile`: False
381
+ - `torch_compile_backend`: None
382
+ - `torch_compile_mode`: None
383
+ - `dispatch_batches`: None
384
+ - `split_batches`: None
385
+ - `include_tokens_per_second`: False
386
+ - `include_num_input_tokens_seen`: False
387
+ - `neftune_noise_alpha`: None
388
+ - `optim_target_modules`: None
389
+ - `batch_eval_metrics`: False
390
+ - `eval_on_start`: False
391
+ - `use_liger_kernel`: False
392
+ - `eval_use_gather_object`: False
393
+ - `batch_sampler`: batch_sampler
394
+ - `multi_dataset_batch_sampler`: round_robin
395
+
396
+ </details>
397
+
398
+ ### Training Logs
399
+ | Epoch | Step | Training Loss |
400
+ |:------:|:----:|:-------------:|
401
+ | 0.9141 | 500 | 0.0055 |
402
+
403
+
404
+ ### Framework Versions
405
+ - Python: 3.10.14
406
+ - Sentence Transformers: 3.2.0
407
+ - Transformers: 4.45.1
408
+ - PyTorch: 2.4.0
409
+ - Accelerate: 0.34.2
410
+ - Datasets: 3.0.1
411
+ - Tokenizers: 0.20.0
412
+
413
+ ## Citation
414
+
415
+ ### BibTeX
416
+
417
+ #### Sentence Transformers
418
+ ```bibtex
419
+ @inproceedings{reimers-2019-sentence-bert,
420
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
421
+ author = "Reimers, Nils and Gurevych, Iryna",
422
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
423
+ month = "11",
424
+ year = "2019",
425
+ publisher = "Association for Computational Linguistics",
426
+ url = "https://arxiv.org/abs/1908.10084",
427
+ }
428
+ ```
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+
430
+ <!--
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+ ## Glossary
432
+
433
+ *Clearly define terms in order to be accessible across audiences.*
434
+ -->
435
+
436
+ <!--
437
+ ## Model Card Authors
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+
439
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
440
+ -->
441
+
442
+ <!--
443
+ ## Model Card Contact
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+
445
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_name_or_path": "BAAI/bge-large-en-v1.5",
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+ "architectures": [
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+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "LABEL_0"
14
+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "LABEL_0": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.45.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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