Fine-tuned model for resume-job section matching

ae1014e verified 7 months ago

42.6 kB

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:958
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: >-
      Develop, train, and fine-tune embedding models for improved resume
      matching. Implement and evaluate various model architectures (e.g.,
      transformers, Siamese networks) using Python and relevant libraries (e.g.,
      TensorFlow, PyTorch). Analyze and interpret model performance metrics,
      identifying areas for improvement. Collaborate with data scientists and
      engineers to integrate models into production systems. Optimize model
      performance for speed and accuracy. Stay up-to-date with the latest
      advancements in NLP and embedding techniques.
    sentences:
      - |-
        Skills:
        *   Python, TensorFlow, PyTorch
        *   NLP, Embedding Models, Machine Learning
        *   Web Development, REST APIs

        Experience:
        Software Engineer, Acme Corp (2020 - Present)
        *   Developed and deployed web applications using Python and Django.
        *   Worked on improving search functionality using Elasticsearch.
      - >-
        Skills: Python, scikit-learn, machine learning, data analysis, threat
        modeling, communication, problem-solving. Projects include NLP for text
        classification and cybersecurity risk assessment utilizing Python and
        relevant libraries.
      - >-
        Product Management Intern | ABC Company | June 2022 - August 2022

        *   Analyzed user feedback on the product, identifying pain points and
        areas for improvement.

        *   Collaborated with engineers on A/B testing new features to improve
        user engagement.

        *   Conducted market research and competitive analysis to inform product
        strategy.

        *   Presented product updates and findings to senior management.
  - source_sentence: >-
      Develop, train, and fine-tune embedding models for resume matching,
      focusing on improving accuracy and relevance. This includes experimenting
      with different model architectures, loss functions, and training datasets.
      Evaluate model performance using relevant metrics (e.g., precision,
      recall, F1-score) and identify areas for improvement. Collaborate with
      data scientists and engineers to deploy and maintain models in a
      production environment. Analyze resume and job description data to
      identify patterns and insights that can inform model development. Stay
      up-to-date with the latest advancements in natural language processing
      (NLP) and machine learning, particularly in the area of embedding models
      and their application to HR and recruitment. Design and implement A/B
      tests to validate model improvements. Provide technical guidance and
      mentorship to junior team members.
    sentences:
      - >-
        **Senior Machine Learning Engineer**

        *   **Skills:** Python, TensorFlow, PyTorch, NLP, Embedding Models,
        Resume Parsing, Evaluation Metrics (Precision, Recall, F1), A/B Testing,
        Cloud Platforms (AWS), Model Deployment. 

        *   Developed and deployed a custom BERT-based model for semantic search
        on large datasets. Focused on improving search accuracy and reducing
        latency. Utilized various loss functions and experimented with different
        hyperparameter configurations. 

        *   Led the development of a candidate ranking system using a Siamese
        network architecture. Integrated the model into a production
        environment. 

        *   Conducted A/B tests to validate model improvements and tracked key
        performance indicators. Mentored junior engineers on model development
        and deployment best practices. 

        *   **Projects:**
            *   Semantic Search Optimization: Improved search accuracy by 15% using fine-tuning of BERT. Implemented a system using ElasticSearch.
            *   Candidate Matching System: Designed and implemented a system that matched resumes with job descriptions using NLP and machine learning techniques.
      - |2-
          *   Built and maintained ETL pipelines using Python and Spark for processing large datasets.
          *   Experience with feature engineering to enhance model accuracy.
          *   Collaborated with cross-functional teams to deploy machine learning models.
          *   Monitored model performance metrics and identified areas for optimization.  
          *   Documented code and processes.   

        Skills:
          *   Python, Spark, SQL, AWS, Machine Learning Principles
      - >-
        Senior Software Engineer | Acme Corp | 2018 - Present

        *   Led the development and deployment of a new resume parsing and
        matching engine, significantly improving the accuracy of candidate
        recommendations.

        *   Implemented and evaluated various machine learning models, including
        BERT and Sentence Transformers, for semantic similarity scoring.

        *   Utilized Python, TensorFlow, and scikit-learn for model training,
        evaluation, and deployment.

        *   Improved model performance by 15% by implementing new loss function
        for semantic search and fine-tuning the model using custom datasets.
  - source_sentence: >-
      Develop and maintain the infrastructure for fine-tuning and evaluating
      embedding models for resume matching. This includes data pipeline design,
      model training pipelines, performance monitoring, and A/B testing of
      different model architectures and training strategies. Optimize model
      performance for accuracy and efficiency, considering factors like latency
      and resource consumption. Collaborate with data scientists and product
      managers to understand requirements and translate them into technical
      solutions. Build and maintain documentation for all processes and tools.
    sentences:
      - >-
        Skills:


        *   **Python:** Extensive experience in data manipulation and analysis
        using libraries like Pandas and NumPy. Proficient in developing and
        deploying machine learning models.

        *   **Machine Learning:** Solid understanding of various ML algorithms
        (Regression, Decision Trees, SVM, etc.) and experience with model
        evaluation and selection. Familiar with hyperparameter tuning.

        *   **NLP:** Working knowledge of NLP concepts, including text
        classification and sentiment analysis. Used NLTK and SpaCy for text
        preprocessing and analysis.

        *   **Deep Learning:** Developed and trained Convolutional Neural
        Networks (CNNs) for image recognition and Recurrent Neural Networks
        (RNNs) for sequence data.

        *   **Cloud Computing:** Used AWS for deploying web applications and
        storing large datasets. Experienced with EC2 and S3 services.

        *   **Data Analysis:** Strong analytical skills with the ability to
        extract insights from complex datasets.

        *   **Tools:** TensorFlow, scikit-learn, Git, Docker
      - >-
        Senior Data Engineer | Acme Corp | 2018 - Present

        * Developed and maintained Spark-based data pipelines for processing
        large datasets used in machine learning models.

        * Implemented model monitoring dashboards and alerting systems using
        Prometheus and Grafana.

        * Collaborated with the data science team to deploy models to production
        using Kubernetes.

        * Experience with AWS cloud services, including S3, EMR, and SageMaker.
      - >-
        ### Data Engineering & Machine Learning Projects


        *   **Resume Matching System:** Designed and implemented an end-to-end
        pipeline for processing resumes and matching them to job descriptions.
        Leveraged Elasticsearch for indexing and search.  Improved match
        relevance by 15% by analyzing search query logs and refining scoring
        algorithms. Used Python, Spark, and AWS services.

        *   **Model Training and Evaluation:** Built automated pipelines for
        training and evaluating machine learning models.  Implemented model
        versioning and A/B testing to improve model performance. Monitored model
        performance using Prometheus and Grafana, identifying and resolving
        performance bottlenecks. Skilled in TensorFlow, PyTorch, and
        scikit-learn. Experience in data preprocessing, feature engineering, and
        model selection. Wrote extensive documentation and user guides for
        deployed pipelines. Focused on accuracy and efficiency.

        *   **Data Pipeline Development:** Designed and built scalable data
        pipelines using Apache Kafka and Spark for real-time data processing.
        Maintained high availability and reliability.  Implemented data
        validation and error handling mechanisms. Focused on data quality and
        efficiency.
  - source_sentence: >-
      PhD or Master's degree in Marketing, Data Science, Statistics, or a
      related quantitative field is required. Experience with developing and
      fine-tuning embedding models for semantic similarity and information
      retrieval is highly desirable. A strong understanding of NLP techniques
      (e.g., transformers, word embeddings) and their application to resume
      parsing and candidate matching is essential. Expertise in using Python and
      relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn) is a must.
    sentences:
      - >-
        Education:


        *   **PhD, Marketing** - University of California, Berkeley (2015-2019)
            *   Dissertation: *Predictive Modeling of Consumer Behavior Using Neural Networks* - Focused on advanced statistical modeling techniques, including transformer networks for sentiment analysis. Proficient in Python (TensorFlow, Keras), R, and data visualization.
        *   **MBA** - Harvard Business School (2013). Focused on strategic
        marketing and quantitative analysis. Experience with market research and
        predictive modeling.
      - >-
        Resume Matching and NLP Enthusiast | Recent graduate with a Bachelor's
        degree in Data Science. Passionate about applying machine learning
        techniques to solve real-world problems. Proficient in Python, including
        libraries like Scikit-learn and TensorFlow. Conducted a personal project
        on text classification achieving 85% accuracy. Eager to contribute to
        improving recommendation systems and model accuracy.
      - |-
        ## Software Engineer | Acme Corp | 2020 - Present
        *   Improved search functionality within the company intranet.
        *   Utilized Python and TensorFlow for data analysis and model training.
        *   Worked with vector databases to manage search results.
        *   Participated in the deployment of the search application.
        *   Successfully reduced search latency by 15%.
  - source_sentence: >-
      Developed and maintained core backend services using Python and Django,
      focusing on scalability and efficiency. Implemented RESTful APIs for data
      retrieval and manipulation.  Worked extensively with PostgreSQL for data
      storage and retrieval.  Responsible for optimizing database queries and
      improving API response times.  Experience with model fine-tuning for
      semantic search and document retrieval using pre-trained embedding models
      like Sentence Transformers or similar libraries, specifically for
      improving the relevance of search results and document matching within the
      web application.  Experience using vector databases (e.g., ChromaDB,
      Weaviate) preferred.
    sentences:
      - >-
        Skills: Python (proficient in Pandas, Scikit-learn, and Numpy), Machine
        Learning (classification, regression), NLP fundamentals, familiarity
        with BERT and TF-IDF, data visualization with Matplotlib and Seaborn.
        Experience using AWS S3 and EC2 for data storage and model training.
        Conducted A/B testing on marketing campaigns. Experience with data
        analysis and reporting.
      - >-
        PhD in Computer Science, University of California, Berkeley (2018-2023).
        Dissertation: 'Adversarial Robustness in NLP for Cybersecurity
        Applications.' Focused on fine-tuning BERT for malware detection and
        social engineering attacks. Proficient in Python, TensorFlow, and AWS.
        Published in top-tier NLP and security conferences. Experienced with
        large datasets and model evaluation metrics.


        Master of Science in Cybersecurity, Johns Hopkins University
        (2016-2018). Relevant coursework included Machine Learning, Data Mining,
        and Network Security. Developed a system for anomaly detection using a
        recurrent neural network (RNN). Familiar with Python and cloud computing
        platforms. Good understanding of NLP concepts, but limited experience
        fine-tuning transformer models. Strong understanding of Information
        Security Principles.


        Bachelor of Science in Computer Engineering, Carnegie Mellon University
        (2012-2016). Relevant coursework: Artificial Intelligence, Database
        Management, and Software Engineering. Project experience: Developed a
        web application using Python. No direct experience with fine-tuning NLP
        models, but a strong foundation in programming and data structures. 
        Familiar with cloud infrastructure concepts. Possess CISSP
        certification.
      - >-
        ## Senior Backend Engineer


        *   **ABC Corp** | 2020 - Present

        *   Led development of a new REST API for user authentication and
        profile management using Python and Django.

        *   Managed a PostgreSQL database, optimizing queries and schema design
        for improved performance, resulting in a 20% reduction in average API
        response time.

        *   Improved system scalability through efficient code design and load
        balancing techniques.

        *   Experience using pre-trained embedding models (BERT) for natural
        language processing tasks to improve search accuracy, with focus on
        keyphrase extraction and content similarity comparison for the
        recommendations engine. Proficient in Flask.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: dev evaluation
          type: dev_evaluation
        metrics:
          - type: pearson_cosine
            value: 0.5378933775375572
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6213226022358173
            name: Spearman Cosine
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: test evaluation
          type: test_evaluation
        metrics:
          - type: pearson_cosine
            value: 0.5378933775375572
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6213226022358173
            name: Spearman Cosine

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("anass1209/resume-job-matcher-all-MiniLM-L6-v2")
# Run inference
sentences = [
    'Developed and maintained core backend services using Python and Django, focusing on scalability and efficiency. Implemented RESTful APIs for data retrieval and manipulation.  Worked extensively with PostgreSQL for data storage and retrieval.  Responsible for optimizing database queries and improving API response times.  Experience with model fine-tuning for semantic search and document retrieval using pre-trained embedding models like Sentence Transformers or similar libraries, specifically for improving the relevance of search results and document matching within the web application.  Experience using vector databases (e.g., ChromaDB, Weaviate) preferred.',
    '## Senior Backend Engineer\n\n*   **ABC Corp** | 2020 - Present\n*   Led development of a new REST API for user authentication and profile management using Python and Django.\n*   Managed a PostgreSQL database, optimizing queries and schema design for improved performance, resulting in a 20% reduction in average API response time.\n*   Improved system scalability through efficient code design and load balancing techniques.\n*   Experience using pre-trained embedding models (BERT) for natural language processing tasks to improve search accuracy, with focus on keyphrase extraction and content similarity comparison for the recommendations engine. Proficient in Flask.',
    "PhD in Computer Science, University of California, Berkeley (2018-2023). Dissertation: 'Adversarial Robustness in NLP for Cybersecurity Applications.' Focused on fine-tuning BERT for malware detection and social engineering attacks. Proficient in Python, TensorFlow, and AWS. Published in top-tier NLP and security conferences. Experienced with large datasets and model evaluation metrics.\n\nMaster of Science in Cybersecurity, Johns Hopkins University (2016-2018). Relevant coursework included Machine Learning, Data Mining, and Network Security. Developed a system for anomaly detection using a recurrent neural network (RNN). Familiar with Python and cloud computing platforms. Good understanding of NLP concepts, but limited experience fine-tuning transformer models. Strong understanding of Information Security Principles.\n\nBachelor of Science in Computer Engineering, Carnegie Mellon University (2012-2016). Relevant coursework: Artificial Intelligence, Database Management, and Software Engineering. Project experience: Developed a web application using Python. No direct experience with fine-tuning NLP models, but a strong foundation in programming and data structures.  Familiar with cloud infrastructure concepts. Possess CISSP certification.",
]
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]

Evaluation

Metrics

Semantic Similarity

Datasets: dev_evaluation and test_evaluation
Evaluated with EmbeddingSimilarityEvaluator

Metric	dev_evaluation	test_evaluation
pearson_cosine	0.5379	0.5379
spearman_cosine	0.6213	0.6213

Training Details

Training Dataset

Unnamed Dataset

Size: 958 training samples
Columns: sentence_0, sentence_1, and label

Approximate statistics based on the first 958 samples:

	sentence_0	sentence_1	label
type	string	string	float
details	min: 41 tokens mean: 110.12 tokens max: 234 tokens	min: 25 tokens mean: 134.18 tokens max: 256 tokens	min: 0.5 mean: 0.78 max: 0.96

Samples:

sentence_0	sentence_1	label
`Required skills include experience with embedding models, fine-tuning techniques, Python programming, and knowledge of NLP concepts. Proficiency in libraries like TensorFlow or PyTorch is essential. Familiarity with resume parsing and matching algorithms is a plus. Must be able to analyze performance metrics and iterate on model improvements.`	Skills: Python, TensorFlow, NLP, Embedding Models, Fine-tuning, Resume Matching, Model Evaluation. Experienced in building and deploying machine learning models for text analysis and information retrieval. Proficient in analyzing performance using precision, recall, and F1-score to improve model accuracy. Skills: Python, PyTorch, Natural Language Processing, Text Classification, Machine Learning. Developed several machine learning models using Python and PyTorch for various text related tasks. Good understanding of model evaluation metrics. Technical Skills: Python, Scikit-learn, Data analysis, Data Visualization, Natural Language Processing basics. Projects include text classification and sentiment analysis. Knowledge of model evaluation techniques. Proficient in Python. Familiar with basic machine learning concepts and libraries. Experience with data cleaning and preprocessing. Strong analytical and problem-solving skills. Skills: Python, Pandas, Scikit-learn, Data Preprocessing...	`0.8882194757461548`
`Experience with embedding models and fine-tuning techniques. Ability to analyze resume data and identify relevant keywords for improved matching. Proficiency in Python and experience with relevant libraries like Transformers, Sentence Transformers, and scikit-learn. Knowledge of A/B testing and evaluation metrics (precision, recall, F1-score). Understanding of product management principles and the product development lifecycle is a plus.`	`Skills: * Python (proficient in Pandas, NumPy) * Machine Learning (basic understanding) * Data Analysis * A/B Testing (conducted tests for website optimization) * Excellent communication and presentation skills`	`0.5`
Senior DevOps Engineer to lead the implementation and optimization of our resume matching system. Responsibilities include: Fine-tuning and evaluating embedding models (e.g., Sentence Transformers, BERT) for improved semantic similarity scoring. Developing and maintaining the infrastructure for model training, evaluation, and deployment. Collaborating with data scientists and software engineers to integrate the matching system into our platform. Monitoring model performance and identifying areas for improvement, including data augmentation strategies. Strong experience with Python, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines. Must have proficiency in evaluating model performance metrics (precision, recall, F1-score, AUC) and experience with model versioning and A/B testing.	`## Experience Senior DevOps Engineer`	Acme Corp

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 50
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 50
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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	dev_evaluation_spearman_cosine	test_evaluation_spearman_cosine
1.0	60	-	0.4867	-
2.0	120	-	0.5612	-
3.0	180	-	0.5929	-
4.0	240	-	0.6229	-
5.0	300	-	0.6377	-
6.0	360	-	0.6434	-
7.0	420	-	0.6104	-
8.0	480	-	0.6064	-
8.3333	500	0.0122	-	-
9.0	540	-	0.6005	-
10.0	600	-	0.6064	-
11.0	660	-	0.5973	-
12.0	720	-	0.6097	-
13.0	780	-	0.5907	-
14.0	840	-	0.5870	-
15.0	900	-	0.5989	-
16.0	960	-	0.6018	-
16.6667	1000	0.0019	-	-
17.0	1020	-	0.6208	-
18.0	1080	-	0.6133	-
19.0	1140	-	0.6200	-
20.0	1200	-	0.5960	-
21.0	1260	-	0.5999	-
22.0	1320	-	0.5995	-
23.0	1380	-	0.6177	-
24.0	1440	-	0.6201	-
25.0	1500	0.0009	0.6110	-
26.0	1560	-	0.6184	-
27.0	1620	-	0.6133	-
28.0	1680	-	0.6287	-
29.0	1740	-	0.6200	-
30.0	1800	-	0.6272	-
31.0	1860	-	0.6222	-
32.0	1920	-	0.6199	-
33.0	1980	-	0.6141	-
33.3333	2000	0.0006	-	-
34.0	2040	-	0.6228	-
35.0	2100	-	0.6275	-
36.0	2160	-	0.6167	-
37.0	2220	-	0.6140	-
38.0	2280	-	0.6217	-
39.0	2340	-	0.6280	-
40.0	2400	-	0.6254	-
41.0	2460	-	0.6186	-
41.6667	2500	0.0005	-	-
42.0	2520	-	0.6185	-
43.0	2580	-	0.6242	-
44.0	2640	-	0.6183	-
45.0	2700	-	0.6213	-
46.0	2760	-	0.6220	-
47.0	2820	-	0.6213	-
48.0	2880	-	0.6213	-
49.0	2940	-	0.6214	-
50.0	3000	0.0004	0.6213	-
-1	-1	-	-	0.6213

Framework Versions

Python: 3.11.11
Sentence Transformers: 4.1.0
Transformers: 4.51.1
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.5.0
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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",
}