| | --- |
| | base_model: BAAI/bge-large-en-v1.5 |
| | library_name: sentence-transformers |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:4370 |
| | - loss:CosineSimilarityLoss |
| | widget: |
| | - source_sentence: ' |
| | |
| | Construct: Recognise a linear graph from its shape |
| | |
| | Subject: Finding the Gradient and Intercept of a Line from the Equation |
| | |
| | Question: Use a graphing program (e.g. Desmos) to plot the following pairs of |
| | functions. |
| | |
| | \[ |
| | |
| | y=3 \text { and } y=-2 |
| | |
| | \] |
| | |
| | |
| | Tom says both functions are linear |
| | |
| | |
| | Katie says both functions are vertical lines |
| | |
| | |
| | Who is correct? |
| | |
| | Incorrect Answer: Neither is correct |
| | |
| | Correct Answer: Only |
| | |
| | Tom |
| | |
| | ' |
| | sentences: |
| | - Believes the coefficent of x in an expanded quadratic comes from multiplying the |
| | two numbers in the brackets |
| | - Does not know the properties of a linear graph |
| | - Misremembers the quadratic formula |
| | - source_sentence: ' |
| | |
| | Construct: Multiply two decimals together with the same number of decimal places |
| | |
| | Subject: Multiplying and Dividing with Decimals |
| | |
| | Question: \( 0.6 \times 0.4= \) |
| | |
| | Incorrect Answer: \( 2.4 \) |
| | |
| | Correct Answer: \( 0.24 \) |
| | |
| | ' |
| | sentences: |
| | - When asked to solve simultaneous equations, believes they can just find values |
| | that work in one equation |
| | - Believes the solutions of a quadratic equation are the constants in the factorised |
| | form |
| | - When multiplying decimals, divides by the wrong power of 10 when reinserting the |
| | decimal |
| | - source_sentence: ' |
| | |
| | Construct: Estimate the volume or capacity of an object |
| | |
| | Subject: Volume of Prisms |
| | |
| | Question: Each of these measurements matches one of these objects. ![An image |
| | of 4 objects and 4 measurements. The objects are an egg cup, a cereal box, a chest |
| | of drawers and a piggy bank. And, the measurements are 87 cm^3, 1013 cm^3, 4172 |
| | cm^3 and 197,177 cm^3.]() Which measurement most likely matches the egg cup? |
| | |
| | Incorrect Answer: \( 197177 \mathrm{~cm}^{3} \) |
| | |
| | Correct Answer: \( 87 \mathrm{~cm}^{3} \) |
| | |
| | ' |
| | sentences: |
| | - Confuses quadratic and exponential graphs |
| | - Cannot estimate the relative volume order, for different objects |
| | - Does not know how many days are in a leap year |
| | - source_sentence: ' |
| | |
| | Construct: Carry out division problems involving one negative integer |
| | |
| | Subject: Multiplying and Dividing Negative Numbers |
| | |
| | Question: \( 12 \div(-4)= \) |
| | |
| | Incorrect Answer: \( 3 \) |
| | |
| | Correct Answer: \( -3 \) |
| | |
| | ' |
| | sentences: |
| | - Believes dividing a positive by a negative gives a positive answer |
| | - Believes -a is always smaller than a, ignoring the possibility that a is negative |
| | - Subtracts instead of divides |
| | - source_sentence: ' |
| | |
| | Construct: Construct frequency tables |
| | |
| | Subject: Frequency tables |
| | |
| | Question: Dave has recorded the number of pets his classmates have in the frequency |
| | table on the right. \begin{tabular}{|c|c|} |
| | |
| | \hline Number of pets & Frequency \\ |
| | |
| | \hline \( 0 \) & \( 4 \) \\ |
| | |
| | \hline \( 1 \) & \( 6 \) \\ |
| | |
| | \hline \( 2 \) & \( 3 \) \\ |
| | |
| | \hline \( 3 \) & \( 2 \) \\ |
| | |
| | \hline \( 4 \) & \( 5 \) \\ |
| | |
| | \hline |
| | |
| | \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? |
| | |
| | Incorrect Answer: Number of pets - |
| | |
| | Frequency |
| | |
| | Correct Answer: Number of pets \( x \) Frequency |
| | |
| | ' |
| | sentences: |
| | - Subtracts rather than multiplies when calculating total frequency |
| | - Does not follow the arrows through a function machine, changes the order of the |
| | operations asked. |
| | - 'Believes the intersection in a prime factor venn diagram does not contribute |
| | to the size of the number represented by a circle ' |
| | --- |
| | |
| | # SentenceTransformer based on BAAI/bge-large-en-v1.5 |
| |
|
| | 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. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** Sentence Transformer |
| | - **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 1024 tokens |
| | - **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': 512, 'do_lower_case': True}) with Transformer model: BertModel |
| | (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}) |
| | (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("VaggP/bge-fine-tuned") |
| | # Run inference |
| | sentences = [ |
| | '\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', |
| | 'Subtracts rather than multiplies when calculating total frequency', |
| | 'Does not follow the arrows through a function machine, changes the order of the operations asked.', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 1024] |
| | |
| | # 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: 4,370 training samples |
| | * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | label | |
| | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 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> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------| |
| | | <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> | |
| | | <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> | |
| | | <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> | |
| | * 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 |
| |
|
| | - `num_train_epochs`: 1 |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: no |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 8 |
| | - `per_device_eval_batch_size`: 8 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 5e-05 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1 |
| | - `num_train_epochs`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.0 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `no_cuda`: False |
| | - `use_cpu`: False |
| | - `use_mps_device`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `jit_mode_eval`: False |
| | - `use_ipex`: False |
| | - `bf16`: False |
| | - `fp16`: False |
| | - `fp16_opt_level`: O1 |
| | - `half_precision_backend`: auto |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: 0 |
| | - `ddp_backend`: None |
| | - `tpu_num_cores`: None |
| | - `tpu_metrics_debug`: False |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_min_num_params`: 0 |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `fsdp_transformer_layer_cls_to_wrap`: None |
| | - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch |
| | - `optim_args`: None |
| | - `adafactor`: False |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `ddp_find_unused_parameters`: None |
| | - `ddp_bucket_cap_mb`: None |
| | - `ddp_broadcast_buffers`: False |
| | - `dataloader_pin_memory`: True |
| | - `dataloader_persistent_workers`: False |
| | - `skip_memory_metrics`: True |
| | - `use_legacy_prediction_loop`: False |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: False |
| | - `hub_always_push`: False |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `eval_do_concat_batches`: True |
| | - `fp16_backend`: auto |
| | - `push_to_hub_model_id`: None |
| | - `push_to_hub_organization`: None |
| | - `mp_parameters`: |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `torchdynamo`: None |
| | - `ray_scope`: last |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `dispatch_batches`: None |
| | - `split_batches`: None |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: False |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `use_liger_kernel`: False |
| | - `eval_use_gather_object`: False |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | |
| | |:------:|:----:|:-------------:| |
| | | 0.9141 | 500 | 0.0055 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.10.14 |
| | - Sentence Transformers: 3.2.0 |
| | - Transformers: 4.45.1 |
| | - PyTorch: 2.4.0 |
| | - Accelerate: 0.34.2 |
| | - Datasets: 3.0.1 |
| | - Tokenizers: 0.20.0 |
| | |
| | ## 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.* |
| | --> |
| | |
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| | ## Model Card Contact |
| | |
| | *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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