Add new CrossEncoder model

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- cross-encoder
+- sentence-transformers
+- text-classification
+- sentence-pair-classification
+- semantic-similarity
+- semantic-search
+- retrieval
+- reranking
+- generated_from_trainer
+- dataset_size:1452533
+- loss:MultipleNegativesRankingLoss
+base_model: cross-encoder/ms-marco-MiniLM-L6-v2
+datasets:
+- redis/langcache-sentencepairs-v1
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+---
+
+# Redis fine-tuned CrossEncoder model for semantic caching on LangCache
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for sentence pair classification.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+- **Training Dataset:**
+    - [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("redis/langcache-reranker-v1-miniL6-softmnrl-triplet")
+# Get scores for pairs of texts
+pairs = [
+    [' What high potential jobs are there other than computer science?', ' What high potential jobs are there other than computer science?'],
+    [' Would India ever be able to develop a missile system like S300 or S400 missile?', ' Would India ever be able to develop a missile system like S300 or S400 missile?'],
+    [' water from the faucet is being drunk by a yellow dog', 'A yellow dog is drinking water from the faucet'],
+    [' water from the faucet is being drunk by a yellow dog', 'The yellow dog is drinking water from a bottle'],
+    ['! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``', '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    ' What high potential jobs are there other than computer science?',
+    [
+        ' What high potential jobs are there other than computer science?',
+        ' Would India ever be able to develop a missile system like S300 or S400 missile?',
+        'A yellow dog is drinking water from the faucet',
+        'The yellow dog is drinking water from a bottle',
+        '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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
+
+#### LangCache Sentence Pairs (subsets=['all'], train+val=True)
+
+* Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
+* Size: 1,452,533 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                                           | positive                                                                                        | negative_1                                                                                     |
+  |:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | type    | string                                                                                           | string                                                                                          | string                                                                                         |
+  | details | <ul><li>min: 24 characters</li><li>mean: 114.25 characters</li><li>max: 268 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 114.1 characters</li><li>max: 226 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 93.04 characters</li><li>max: 234 characters</li></ul> |
+* Samples:
+  | anchor                                                                                                                                   | positive                                                                                                                                 | negative_1                                                                               |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
+  | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code>                                                            | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code>                                                            | <code>Are there many Canadians living and working illegally in the United States?</code> |
+  | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code>Is there any tricks for straight lines mcqs?</code>                                |
+  | <code> Can I pay with a debit card on PayPal?</code>                                                                                     | <code> Can I pay with a debit card on PayPal?</code>                                                                                     | <code>Can you transfer PayPal funds onto a debit card/credit card?</code>                |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "num_negatives": 1,
+      "activation_fn": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### LangCache Sentence Pairs (split=test)
+
+* Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
+* Size: 110,066 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                                         | positive                                                                                       | negative_1                                                                                      |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                         | string                                                                                          |
+  | details | <ul><li>min: 3 characters</li><li>mean: 97.95 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 97.03 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 74.49 characters</li><li>max: 295 characters</li></ul> |
+* Samples:
+  | anchor                                                                                        | positive                                                                                      | negative_1                                                                                     |
+  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
+  | <code> What high potential jobs are there other than computer science?</code>                 | <code> What high potential jobs are there other than computer science?</code>                 | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
+  | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code>                     |
+  | <code> water from the faucet is being drunk by a yellow dog</code>                            | <code>A yellow dog is drinking water from the faucet</code>                                   | <code>Do you get more homework in 9th grade than 8th?</code>                                   |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "num_negatives": 1,
+      "activation_fn": "torch.nn.modules.activation.Sigmoid"
+  }
+  ```
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.0
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## 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

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "bfloat16",
+  "gradient_checkpointing": false,
+  "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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.1.0"
+  },
+  "transformers_version": "4.56.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:34be19140398a1d70986b272a71ace1bf034ea4e88783015370f169f054d8d4c
+size 45439314

special_tokens_map.json

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+{
+  "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

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+{
+  "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,
+  "extra_special_tokens": {},
+  "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]"
+}