Upload fine-tuned model
Browse files- README.md +92 -135
- model.safetensors +1 -1
README.md
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- loss:CosineSimilarityLoss
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base_model: sentence-transformers/all-MiniLM-L6-v2
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widget:
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buy.; Attorney later road drive high could new.; Public near program language..
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Docker; Azure; Linux; JavaScript; React. SQL; DevOps; Linux; Python; TensorFlow;
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C#
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site run fly purpose face.. Yourself address might expect his budget bill.; Later
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top focus guess occur hour.; Have turn quickly help well its.; However research
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visit.; Commercial building especially capital system each.. Python; Machine Learning;
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Terraform; Deep Learning; Java; Cybersecurity; Linux. At exactly story letter
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dream.; Paper experience control author like president girl education.; Education
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fund hear side mother.; Who then more start various draw along.
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to join our growing team and work on exciting projects.. Threat store center scene
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country can quite.; Campaign today degree.; Data when risk citizen common.; Current
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few environment social about page.. Penetration Testing; Java; Node.js; Docker;
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JavaScript. SQL; AWS; CI/CD; JavaScript; Machine Learning; C#
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chance.. Within value ahead.; Class democratic candidate arm.; Region represent
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great note nothing recently low.; Way live according follow walk doctor loss..
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Azure; TensorFlow; JavaScript; Machine Learning; CI/CD; Kubernetes; Terraform.
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Here other next over down seem yourself model.; Discover natural generation traditional
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suddenly management.; Discuss food majority professor.
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- source_sentence: AI Researcher. We are looking for a AI Researcher to join our growing
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team and work on exciting projects.. Improve hard street ask anyone accept history.;
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Heavy a through old nothing various.; Fight clearly safe available similar hot.;
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Movie body accept society heavy six.; Note close bad detail cell.. NoSQL; Azure;
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Terraform; Flask; Django. Deep Learning; NoSQL; Terraform; Python; CI/CD; Flask
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bill so technology. Thousand north particularly difficult. Check social into decade
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thing minute ahead.. Send memory ago full director although morning.; Relationship
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sign front actually forget personal cold name.; Near debate notice their.. SQL;
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AWS; C#; Node.js; Cybersecurity; Machine Learning; React. Produce fly sea.; Middle
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race risk.; Land foot often action brother dinner.; Sign administration use book
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section memory tree.
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- source_sentence: Machine Learning Engineer. We are looking for a Machine Learning
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Engineer to join our growing team and work on exciting projects.. Talk serious
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or mouth night measure.; Article ahead capital no development.; Do minute chance
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employee.; Account impact product land never military main show.. Cybersecurity;
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Terraform; Deep Learning; Python; Linux. Azure; Django; Docker; NoSQL; TypeScript;
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SQL
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sentences:
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they not.. Reflect development forward hand.; Investment fall what guess.; Green
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new instead language board.. Kubernetes; TypeScript; Django; TensorFlow; AWS;
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C#; Deep Learning. Lay tax group message work statement ago.; Can try heart city.;
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Positive social increase throw seat share standard.; Front far prepare.
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- source_sentence: Software Engineer. We are looking for a Software Engineer to join
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our growing team and work on exciting projects.. Suffer class note resource.;
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Guess really character and right scientist behavior election.; Seat force cultural
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arm while.; Single maintain from recently.; Not thing wife focus road.. CI/CD;
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Terraform; DevOps; JavaScript; TypeScript. Docker; Java; Azure; Deep Learning;
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AWS; Node.js
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sentences:
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participant sense.; Him station low happen available woman parent.; Measure recent
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rock say city indeed allow value.
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war.; Meeting certainly leader party heavy mind authority nearly.; Sister certain
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any itself.; Paper top at area provide.. Cybersecurity; React; C#; TensorFlow;
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Deep Learning; Penetration Testing; DevOps. Food safe wide key.; Word identify
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cup life clear.
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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---
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@@ -180,9 +125,9 @@ from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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* Size: 5,000 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
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| type | string
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| details | <ul><li>min:
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* Samples:
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| sentence_0
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| <code>
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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```json
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{
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `num_train_epochs`:
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- `multi_dataset_batch_sampler`: round_robin
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#### All Hyperparameters
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1
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- `num_train_epochs`:
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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</details>
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### Training Logs
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| Epoch
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| 1.5974
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| 3.1949
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### Framework Versions
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- loss:CosineSimilarityLoss
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base_model: sentence-transformers/all-MiniLM-L6-v2
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widget:
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- source_sentence: looking Product Manager expertise AWS Cybersecurity JavaScript
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Cloud Architecture candidate responsible designing implementing maintaining solutions
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using modern technologies
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sentences:
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- Emily Barry professional skilled JavaScript Machine Learning Kubernetes Computer
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Vision Experienced working multiple projects involving cloud technologies modern
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software development practices
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- Stephen Baker professional skilled React AWS Node.js NLP Experienced working multiple
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projects involving cloud technologies modern software development practices
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- James Jackson professional skilled Node.js Cybersecurity Kubernetes Docker Experienced
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working multiple projects involving cloud technologies modern software development
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practices
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- source_sentence: looking Software Engineer expertise AWS TensorFlow NLP Node.js
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candidate responsible designing implementing maintaining solutions using modern
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technologies
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sentences:
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- Jennifer Thompson professional skilled JavaScript TensorFlow Computer Vision Django
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- Lisa Bell professional skilled Python TensorFlow Computer Vision Machine Learning
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- Susan Rogers professional skilled Docker Cybersecurity Machine Learning Python
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- source_sentence: looking DevOps Engineer expertise Cybersecurity Machine Learning
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SQL TensorFlow candidate responsible designing implementing maintaining solutions
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using modern technologies
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sentences:
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- Kenneth Jones professional skilled NLP Node.js Cybersecurity Cloud Architecture
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- Matthew Mcintyre professional skilled NoSQL Kubernetes React Docker Experienced
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working multiple projects involving cloud technologies modern software development
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practices
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- William Wilson professional skilled SQL Kubernetes CI/CD Security Analysis Experienced
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working multiple projects involving cloud technologies modern software development
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practices
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- source_sentence: looking Software Engineer expertise Cybersecurity NLP SQL Django
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candidate responsible designing implementing maintaining solutions using modern
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technologies
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sentences:
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- Daniel Stewart professional skilled JavaScript Python Cybersecurity TensorFlow
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- Kristy Massey MD professional skilled Django Security Analysis JavaScript Cybersecurity
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- Melanie Sutton professional skilled Django CI/CD JavaScript SQL Experienced working
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multiple projects involving cloud technologies modern software development practices
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- source_sentence: looking AI Researcher expertise CI/CD Docker TensorFlow JavaScript
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candidate responsible designing implementing maintaining solutions using modern
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technologies
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sentences:
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- Dr. William Ramirez professional skilled NoSQL React CI/CD Cloud Architecture
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Experienced working multiple projects involving cloud technologies modern software
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development practices
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- Rebecca Wiley professional skilled Python Kubernetes Node.js JavaScript Experienced
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working multiple projects involving cloud technologies modern software development
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practices
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- Roberta Graham professional skilled Flask Machine Learning Node.js Docker Experienced
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working multiple projects involving cloud technologies modern software development
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practices
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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---
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'looking AI Researcher expertise CI/CD Docker TensorFlow JavaScript candidate responsible designing implementing maintaining solutions using modern technologies',
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'Roberta Graham professional skilled Flask Machine Learning Node.js Docker Experienced working multiple projects involving cloud technologies modern software development practices',
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'Rebecca Wiley professional skilled Python Kubernetes Node.js JavaScript Experienced working multiple projects involving cloud technologies modern software development practices',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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* Size: 5,000 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: 20 tokens</li><li>mean: 24.72 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 26.26 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 0.4</li><li>mean: 0.71</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>looking AI Researcher expertise CI/CD Python Computer Vision Flask candidate responsible designing implementing maintaining solutions using modern technologies</code> | <code>Deanna Gibson professional skilled Security Analysis Node.js Machine Learning Kubernetes Experienced working multiple projects involving cloud technologies modern software development practices</code> | <code>0.481</code> |
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| <code>looking Machine Learning Engineer expertise AWS Kubernetes Python Django candidate responsible designing implementing maintaining solutions using modern technologies</code> | <code>Amanda Johnson professional skilled AWS NLP Node.js Security Analysis Experienced working multiple projects involving cloud technologies modern software development practices</code> | <code>0.982</code> |
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| <code>looking Cybersecurity Analyst expertise JavaScript Python Node.js NoSQL candidate responsible designing implementing maintaining solutions using modern technologies</code> | <code>Alicia Patton professional skilled Node.js TensorFlow SQL NoSQL Experienced working multiple projects involving cloud technologies modern software development practices</code> | <code>0.597</code> |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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```json
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{
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `num_train_epochs`: 30
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- `multi_dataset_batch_sampler`: round_robin
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#### All Hyperparameters
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1
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- `num_train_epochs`: 30
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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</details>
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### Training Logs
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| Epoch | Step | Training Loss |
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|:-------:|:----:|:-------------:|
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| 1.5974 | 500 | 0.0324 |
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| 3.1949 | 1000 | 0.0298 |
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| 4.7923 | 1500 | 0.028 |
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| 6.3898 | 2000 | 0.025 |
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| 7.9872 | 2500 | 0.0229 |
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| 9.5847 | 3000 | 0.0198 |
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| 11.1821 | 3500 | 0.0179 |
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| 12.7796 | 4000 | 0.0156 |
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| 14.3770 | 4500 | 0.014 |
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| 15.9744 | 5000 | 0.0127 |
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| 17.5719 | 5500 | 0.0115 |
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| 19.1693 | 6000 | 0.0104 |
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| 20.7668 | 6500 | 0.0098 |
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| 22.3642 | 7000 | 0.009 |
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| 23.9617 | 7500 | 0.0086 |
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| 25.5591 | 8000 | 0.0082 |
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| 27.1565 | 8500 | 0.0078 |
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| 28.7540 | 9000 | 0.0076 |
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### Framework Versions
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model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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size 90864192
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version https://git-lfs.github.com/spec/v1
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oid sha256:10155879dc23fd7de75a79da5c7769b7f20cbfee7e011e992069584d86ab926c
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size 90864192
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