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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:100
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v1.5
widget:
- source_sentence: "func SetFactory(ctx context.Context, f Factory) context.Context\
\ {\n\treturn"
sentences:
- rm -r path
- 'Transforms an array into a DateTime.
@param array $value Array value.
@return DateTime DateTime value.'
- ' context.WithValue(ctx, &clockKey, f)
}'
- source_sentence: "public function hyvesTipUrl($title, $body, $categoryId = 12, $rating\
\ = 5) {\n\n $url = 'http://www.hyves-share.nl/button/tip/?tipcategoryid=%s&rating=%s&title=%s&body=%s';\n"
sentences:
- " by a TLS client to\n\t// authenticate itself to the TLS server.\n\ttemplate.ExtKeyUsage\
\ = append(template.ExtKeyUsage, x509.ExtKeyUsageClientAuth)\n\n\tt := time.Now().UnixNano()\n\
\ttemplate.SerialNumber = pki.BuildPKISerial(t)\n\n\tcertificate, err := pki.SignNewCertificate(privateKey,\
\ template, caCert.Certificate, caKey)\n\tif err != nil {\n\t\treturn nil, fmt.Errorf(\"\
error signing certificate for master kubelet: %v\", err)\n\t}\n\n\tcaBytes, err\
\ := caCert.AsBytes()\n\tif err != nil {\n\t\treturn nil, fmt.Errorf(\"failed\
\ to get certificate authority data: %s\", err)\n\t}\n\tcertBytes, err := certificate.AsBytes()\n\
\tif err != nil {\n\t\treturn nil, fmt.Errorf(\"failed to get certificate data:\
\ %s\", err)\n\t}\n\tkeyBytes, err := privateKey.AsBytes()\n\tif err != nil {\n\
\t\treturn nil, fmt.Errorf(\"failed to get private key data: %s\", err)\n\t}\n\
\n\tcontent, err := b.BuildKubeConfig(\"kubelet\", caBytes, certBytes, keyBytes)\n\
\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\treturn &nodetasks.File{\n\t\t\
Path: b.KubeletKubeConfig(),\n\t\tContents: fi.NewStringResource(content),\n\
\t\tType: nodetasks.FileType_File,\n\t\tMode: s(\"600\"),\n\t}, nil\n}"
- 'Executes the current query and returns the response
@throws \Cassandra\Response\Exception
@return \Cassandra\Response'
- " $title = $title;\n $body = $body;\n return sprintf($url,\
\ $categoryId, $rating, $title, $body);\n }"
- source_sentence: "public function get($key, $default = null, $dot_syntax = true)\n\
\ {\n if ($dot_syntax === true) {\n $paths = explode('.',\
\ $key);\n $node =& $this->_data;\n \n foreach\
\ ($paths as $path) {\n if (!is_array($node) || !isset($node[$path]))\
\ {\n // error occurred\n return $default;\n\
\ }\n $node =& $node[$path];\n }\n \
\ \n return $node;\n \n } else {\n \
\ \n return isset($this->_data[$key]) ? $this->_data[$key] :\
\ $default;\n \n }\n }"
sentences:
- // PrintShortName turns a pkix.Name into a string of RDN tuples.
- "Here is the code to create an array, add elements, sort in ascending order, and\
\ print the elements in reverse order in Java:\n\n```java\nimport java.util.Arrays;\n\
\npublic class Main {\n public static void main(String[] args) {\n //\
\ Create an array\n int[] array = {5, 7, 3};\n\n // Sort the array\
\ in ascending order\n Arrays.sort(array);\n\n // Print the elements\
\ in reverse order\n for (int i = array.length - 1; i >= 0; i--) {\n \
\ System.out.println(array[i]);\n }\n }\n}\n```\n\nOutput:\n\
```\n7\n5\n3\n```\n\nIn the code above, we import the `Arrays` class from the\
\ `java.util` package to use the `sort()` method for sorting the array. We create\
\ an integer array `array` with the given elements. The `Arrays.sort(array)` method\
\ sorts the array in ascending order. Finally, we loop through the array in reverse\
\ order starting from the last index (`array.length - 1`) and print each element\
\ using `System.out.println()`."
- 'Returns a single item from the collection data.
@param string $key
@return mixed'
- source_sentence: "def iter(self, query, *parameters, **kwargs):\n \"\"\"\
Returns a generator for records from the query.\"\"\"\n cursor = self._cursor()\n\
\ try:\n self._execute(cursor, query, parameters or None, kwargs)\n\
\ if cursor.description:\n column_names = [column.name\
\ for column in cursor.description]\n while True:\n \
\ record = cursor.fetchone()\n if not record:\n \
\ break\n yield Row(zip(column_names, record))\n\
\ raise StopIteration\n\n except:\n cursor.close()\n\
\ raise"
sentences:
- "def exit(exit_code=0):\n r\"\"\"A function to support exiting from exit hooks.\n\
\n Could also be used to exit from the calling scripts in a thread safe manner.\n\
\ \"\"\"\n core.processExitHooks()\n\n if state.isExitHooked and not hasattr(sys,\
\ 'exitfunc'): # The function is called from the exit hook\n sys.stderr.flush()\n\
\ sys.stdout.flush()\n os._exit(exit_code) #pylint: disable=W0212\n\n sys.exit(exit_code)"
- Returns a generator for records from the query.
- " \"\"\"\n\n url = self.file['url']\n args = ['{0}={1}'.format(k,\
\ v) for k, v in kwargs.items()]\n\n if args:\n url += '?{0}'.format('&'.join(args))\n\
\n return url"
- source_sentence: What is the total CO2 emission from all aquaculture farms in the
year 2021?
sentences:
- " && value.size == value.uniq.size\n else\n result\n end\n \
\ end"
- "\n\treturn c.postJSON(\"joberror\", args)\n}"
- SELECT SUM(co2_emission) FROM co2_emission WHERE year = 2021;
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). It maps sentences & paragraphs to a 768-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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision e5cf08aadaa33385f5990def41f7a23405aec398 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'NomicBertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("JahnaviKumar/nomic-embed-text1.5-ftcode")
# Run inference
queries = [
"What is the total CO2 emission from all aquaculture farms in the year 2021?",
]
documents = [
'SELECT SUM(co2_emission) FROM co2_emission WHERE year = 2021;',
'\n\treturn c.postJSON("joberror", args)\n}',
' && value.size == value.uniq.size\n else\n result\n end\n end',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7075, 0.3913, 0.3213]])
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 100 training samples
* Columns: <code>query</code> and <code>corpus</code>
* Approximate statistics based on the first 100 samples:
| | query | corpus |
|:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 138.88 tokens</li><li>max: 1004 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 95.76 tokens</li><li>max: 1151 tokens</li></ul> |
* Samples:
| query | corpus |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>def add_data_file(data_files, target, source):<br> """Add an entry to data_files"""<br> for t, f in data_files:<br> if t == target:<br> break<br> else:<br> </code> | <code> data_files.append((target, []))<br> f = data_files[-1][1]<br> if source not in f:<br> f.append(source)</code> |
| <code>function verify (token, options) {<br> options = options \|\| {}<br> options.issuer = options.issuer \|\| this.issuer<br> options.client_id = options.client_id \|\| this.client_id<br> options.client_secret = options.client_secret \|\| this.client_secret<br> options.scope = options.scope \|\| this.scope<br> options.key = options.key \|\| this.jwks.sig<br><br> return new Promise(function (resolve, reject) {<br> AccessToken.verify(token, options, function (err, claims) {<br> if (err) { return reject(err) }<br> resolve(claims)<br> })<br> })<br>}</code> | <code>Verifies a given OIDC token<br>@method verify<br>@param token {String} JWT AccessToken for OpenID Connect (base64 encoded)<br>@param [options={}] {Object} Options hashmap<br>@param [options.issuer] {String} OIDC Provider/Issuer URL<br>@param [options.key] {Object} Issuer's public key for signatures (jwks.sig)<br>@param [options.client_id] {String}<br>@param [options.client_secret {String}<br>@param [options.scope] {String}<br>@throws {UnauthorizedError} HTTP 401 or 403 errors (invalid tokens etc)<br>@return {Promise}</code> |
| <code>def _combine_lines(self, lines):<br> """<br> Combines a list of JSON objects into one JSON object.<br> """<br> </code> | <code> lines = filter(None, map(lambda x: x.strip(), lines))<br> return '[' + ','.join(lines) + ']'</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 5.1.1
- Transformers: 4.54.1
- PyTorch: 2.9.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.2.0
- Tokenizers: 0.21.4
## 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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