Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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
+}

README.md

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+---
+base_model: BAAI/bge-small-en-v1.5
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2442
+- loss:MultipleNegativesSymmetricRankingLoss
+widget:
+- source_sentence: Carry out a subtraction problem with positive integers where the
+    answer is less than 0 598-1000= This problem cannot be solved
+  sentences:
+  - Rounds to the wrong degree of accuracy (rounds too much)
+  - When subtracting fractions, subtracts the numerators and denominators
+  - Believes it is impossible to subtract a bigger number from a smaller number
+- source_sentence: Given the sketch of a curve in the form (x + a)(x + b), work out
+    its factorised form Which of the following could be the equation of this curve?
+    ![A graph of a quadratic curve that crosses the x axis at (1,0) and (3,0) and
+    crosses the y axis at (0,3).]() y=(x+1)(x+3)
+  sentences:
+  - Does not use the associative property of multiplication to find other factors
+    of a number
+  - Believes they only need to multiply the first and last pairs of terms when expanding
+    double brackets
+  - Forgets to swap the sign of roots when placing into brackets
+- source_sentence: For a given output find the input of a function machine ![Image
+    of a function machine. The function is add one third, and the output is 7]() What
+    is the input of this function machine? 7 1/3
+  sentences:
+  - When finding an input of a function machine thinks you apply the operations given
+    rather than the inverse operation.
+  - Believes the solution to mx + c = a is the y intercept of y = mx +c
+  - Squares when asked to find the square root
+- source_sentence: Count a number of objects 1,3,5,7, … ? Which pattern matches the
+    sequence above? ![A sequence of 4 patterns. The first pattern is 1 green dot.
+    The second pattern is green dots arranged in a 2 by 2 square shape. The third
+    pattern is green dots arranged in a 3 by 3 square shape. The fourth pattern is
+    green dots arranged in a 4 by 4 square shape. ]()
+  sentences:
+  - 'Subtracts instead of adds when answering worded problems '
+  - When multiplying a decimal less than 1 by an integer, gives an answer 10 times
+    smaller than it should be
+  - When given a linear sequence, cannot match it to a visual pattern
+- source_sentence: Express one quantity as a fraction of another A group of 8 friends
+    share £6 equally. What fraction of the money do they each get? 1/8
+  sentences:
+  - Thinks the fraction 1/n can express sharing any number of items between n people
+  - 'Does not understand that in the ratio 1:n the total number of parts would be
+    1+n '
+  - Does not recognise the distributive property
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the csv dataset. 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **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': 384, '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("Gurveer05/bge-small-eedi-2024")
+# Run inference
+sentences = [
+    'Express one quantity as a fraction of another A group of 8 friends share £6 equally. What fraction of the money do they each get? 1/8',
+    'Thinks the fraction 1/n can express sharing any number of items between n people',
+    'Does not recognise the distributive property',
+]
+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
+
+#### csv
+
+* Dataset: csv
+* Size: 2,442 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                           | sentence2                                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                            |
+  | details | <ul><li>min: 13 tokens</li><li>mean: 56.55 tokens</li><li>max: 306 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.13 tokens</li><li>max: 40 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                                                                                                     | sentence2                                                                                                                     |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Calculate the distance travelled using a speed-time graph Here is a speed-time graph for a car. Which of the following gives the best estimate for the distance travelled between 8 and 10 seconds? ![A graph showing time in seconds on the x axis and speed in metres per second on the y axis. The curve passes through the points (8,15) and (10,24)]() 48 m</code> | <code>Believes that when finding area under graph you can use the upper y value rather than average of upper and lower</code> |
+  | <code>Add proper fractions with the same denominator Work out: 4/11+7/11 Write your answer in its simplest form. 11/11</code>                                                                                                                                                                                                                                                 | <code>Forgot to simplify the fraction</code>                                                                                  |
+  | <code>Count a number of objects 1,3,5,7, … ? Which pattern matches the sequence above? ![A sequence of 4 patterns. The first pattern is 1 green dot. The second pattern is green dots arranged in a 2 by 2 square shape. The third pattern is green dots arranged in a 3 by 3 square shape. The fourth pattern is green dots arranged in a 4 by 4 square shape. ]()</code>    | <code>When given a linear sequence, cannot match it to a visual pattern</code>                                                |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `num_train_epochs`: 20
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 20
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: 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`: True
+- `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
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch       | Step    | Training Loss |
+|:-----------:|:-------:|:-------------:|
+| 0.3846      | 15      | 2.5015        |
+| 0.7436      | 29      | -             |
+| 0.7692      | 30      | 1.9614        |
+| 1.1538      | 45      | 1.5138        |
+| 1.4872      | 58      | -             |
+| 1.5385      | 60      | 1.4717        |
+| 1.9231      | 75      | 1.2504        |
+| 2.2308      | 87      | -             |
+| 2.3077      | 90      | 1.0932        |
+| 2.6923      | 105     | 1.0067        |
+| 2.9744      | 116     | -             |
+| 3.0769      | 120     | 0.7748        |
+| 3.4615      | 135     | 0.7952        |
+| 3.7179      | 145     | -             |
+| 3.8462      | 150     | 0.7165        |
+| 4.2308      | 165     | 0.5502        |
+| 4.4615      | 174     | -             |
+| 4.6154      | 180     | 0.6062        |
+| 5.0         | 195     | 0.4444        |
+| 5.2051      | 203     | -             |
+| 5.3846      | 210     | 0.4759        |
+| 5.7692      | 225     | 0.4203        |
+| 5.9487      | 232     | -             |
+| 6.1538      | 240     | 0.3599        |
+| 6.5385      | 255     | 0.3613        |
+| 6.6923      | 261     | -             |
+| 6.9231      | 270     | 0.3255        |
+| 7.3077      | 285     | 0.2856        |
+| 7.4359      | 290     | -             |
+| 7.6923      | 300     | 0.2756        |
+| 8.0769      | 315     | 0.2199        |
+| 8.1795      | 319     | -             |
+| 8.4615      | 330     | 0.2587        |
+| 8.8462      | 345     | 0.2293        |
+| 8.9231      | 348     | -             |
+| 9.2308      | 360     | 0.1848        |
+| 9.6154      | 375     | 0.2232        |
+| 9.6667      | 377     | -             |
+| 10.0        | 390     | 0.1755        |
+| 10.3846     | 405     | 0.1898        |
+| 10.4103     | 406     | -             |
+| 10.7692     | 420     | 0.1673        |
+| 11.1538     | 435     | 0.1546        |
+| 11.5385     | 450     | 0.1692        |
+| 11.8974     | 464     | -             |
+| 11.9231     | 465     | 0.1471        |
+| 12.3077     | 480     | 0.1299        |
+| 12.6410     | 493     | -             |
+| 12.6923     | 495     | 0.1269        |
+| 13.0769     | 510     | 0.1201        |
+| 13.3846     | 522     | -             |
+| 13.4615     | 525     | 0.1306        |
+| 13.8462     | 540     | 0.125         |
+| 14.1282     | 551     | -             |
+| 14.2308     | 555     | 0.099         |
+| 14.6154     | 570     | 0.1354        |
+| 14.8718     | 580     | -             |
+| 15.0        | 585     | 0.0967        |
+| 15.3846     | 600     | 0.1159        |
+| 15.6154     | 609     | -             |
+| 15.7692     | 615     | 0.0996        |
+| 16.1538     | 630     | 0.0933        |
+| 16.3590     | 638     | -             |
+| 16.5385     | 645     | 0.1007        |
+| 16.9231     | 660     | 0.0972        |
+| 17.1026     | 667     | -             |
+| 17.3077     | 675     | 0.0978        |
+| 17.6923     | 690     | 0.0874        |
+| **17.8462** | **696** | **-**         |
+| 18.0769     | 705     | 0.0843        |
+| 18.4615     | 720     | 0.0986        |
+| 18.5897     | 725     | -             |
+| 18.8462     | 735     | 0.0862        |
+| 19.2308     | 750     | 0.0751        |
+| 19.3333     | 754     | -             |
+| 19.6154     | 765     | 0.1038        |
+| 20.0        | 780     | 0.0791        |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.0
+- PyTorch: 2.4.0
+- Accelerate: 0.33.0
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+<!--
+## 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 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2684d156a965016095e65deb5b20aed1011c5f7ae1bc4a36cbf40f93025db1eb
+size 133462128

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": 512,
+  "do_lower_case": true
+}

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 +1,57 @@
+{
+  "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": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}