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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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+ language:
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+ - en
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+ - pl
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+ model-index:
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+ - name: 2024-06-24_22-31-28_epoch_31
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+ results:
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+ - dataset:
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+ config: default
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+ name: MTEB AllegroReviews
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+ revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
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+ split: test
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+ type: PL-MTEB/allegro-reviews
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+ metrics:
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+ - type: accuracy
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+ value: 23.598409542743536
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+ - type: f1
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+ value: 21.0752317645165
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: default
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+ name: MTEB CBD
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+ revision: 36ddb419bcffe6a5374c3891957912892916f28d
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+ split: test
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+ type: PL-MTEB/cbd
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+ metrics:
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+ - type: accuracy
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+ value: 56.43
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+ - type: ap
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+ value: 15.427748641552656
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+ - type: f1
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+ value: 46.99394483538345
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: default
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+ name: MTEB CDSC-E
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+ revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
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+ split: test
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+ type: PL-MTEB/cdsce-pairclassification
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+ metrics: []
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+ task:
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+ type: PairClassification
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+ - dataset:
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+ config: default
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+ name: MTEB CDSC-R
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+ revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
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+ split: test
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+ type: PL-MTEB/cdscr-sts
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+ metrics: []
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+ task:
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+ type: STS
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+ - dataset:
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+ config: default
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+ name: MTEB EightTagsClustering
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+ revision: 78b962b130c6690659c65abf67bf1c2f030606b6
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+ split: test
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+ type: PL-MTEB/8tags-clustering
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+ metrics:
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+ - type: v_measure
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+ value: 13.997076379640058
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+ - type: v_measure_std
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+ value: 2.6122726107939154
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+ task:
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+ type: Clustering
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+ - dataset:
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+ config: pl
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+ name: MTEB MassiveIntentClassification (pl)
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+ revision: 4672e20407010da34463acc759c162ca9734bca6
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+ split: test
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+ type: mteb/amazon_massive_intent
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+ metrics:
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+ - type: accuracy
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+ value: 35.09414929388029
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+ - type: f1
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+ value: 32.17534308296389
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: pl
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+ name: MTEB MassiveIntentClassification (pl)
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+ revision: 4672e20407010da34463acc759c162ca9734bca6
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+ split: validation
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+ type: mteb/amazon_massive_intent
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+ metrics:
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+ - type: accuracy
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+ value: 34.928676832267584
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+ - type: f1
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+ value: 31.136038426981095
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: pl
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+ name: MTEB MassiveScenarioClassification (pl)
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+ revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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+ split: test
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+ type: mteb/amazon_massive_scenario
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+ metrics:
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+ - type: accuracy
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+ value: 43.32885003362474
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+ - type: f1
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+ value: 41.42389169421327
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: pl
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+ name: MTEB MassiveScenarioClassification (pl)
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+ revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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+ split: validation
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+ type: mteb/amazon_massive_scenario
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+ metrics:
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+ - type: accuracy
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+ value: 42.75454992621741
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+ - type: f1
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+ value: 41.23705628032583
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: default
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+ name: MTEB PAC
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+ revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
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+ split: test
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+ type: laugustyniak/abusive-clauses-pl
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+ metrics:
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+ - type: accuracy
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+ value: 67.93512887344339
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+ - type: ap
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+ value: 76.17614876171515
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+ - type: f1
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+ value: 64.87387670648395
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: default
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+ name: MTEB PSC
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+ revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
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+ split: test
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+ type: PL-MTEB/psc-pairclassification
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+ metrics: []
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+ task:
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+ type: PairClassification
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+ - dataset:
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+ config: default
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+ name: MTEB PlscClusteringP2P
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+ revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
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+ split: test
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+ type: PL-MTEB/plsc-clustering-p2p
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+ metrics:
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+ - type: v_measure
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+ value: 36.472755666511304
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+ task:
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+ type: Clustering
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+ - dataset:
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+ config: default
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+ name: MTEB PlscClusteringS2S
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+ revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
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+ split: test
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+ type: PL-MTEB/plsc-clustering-s2s
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+ metrics:
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+ - type: v_measure
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+ value: 33.328522408623265
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+ task:
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+ type: Clustering
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+ - dataset:
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+ config: default
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+ name: MTEB PolEmo2.0-IN
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+ revision: d90724373c70959f17d2331ad51fb60c71176b03
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+ split: test
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+ type: PL-MTEB/polemo2_in
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+ metrics:
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+ - type: accuracy
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+ value: 47.60387811634349
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+ - type: f1
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+ value: 48.8296705237083
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: default
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+ name: MTEB PolEmo2.0-OUT
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+ revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
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+ split: test
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+ type: PL-MTEB/polemo2_out
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+ metrics:
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+ - type: accuracy
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+ value: 27.246963562753034
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+ - type: f1
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+ value: 21.095902682283903
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+ task:
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+ type: Classification
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+ - dataset:
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+ config: default
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+ name: MTEB SICK-E-PL
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+ revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
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+ split: test
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+ type: PL-MTEB/sicke-pl-pairclassification
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+ metrics: []
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+ task:
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+ type: PairClassification
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+ - dataset:
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+ config: default
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+ name: MTEB SICK-R-PL
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+ revision: fd5c2441b7eeff8676768036142af4cfa42c1339
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+ split: test
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+ type: PL-MTEB/sickr-pl-sts
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+ metrics: []
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+ task:
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+ type: STS
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+ - dataset:
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+ config: pl
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+ name: MTEB STS22 (pl)
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+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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+ split: test
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+ type: mteb/sts22-crosslingual-sts
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+ metrics: []
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+ task:
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+ type: STS
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+ - dataset:
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+ config: pl
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+ name: MTEB STSBenchmarkMultilingualSTS (pl)
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+ revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
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+ split: dev
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+ type: mteb/stsb_multi_mt
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+ metrics: []
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+ task:
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+ type: STS
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+ - dataset:
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+ config: pl
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+ name: MTEB STSBenchmarkMultilingualSTS (pl)
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+ revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
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+ split: test
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+ type: mteb/stsb_multi_mt
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+ metrics: []
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+ task:
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+ type: STS
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - mteb
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+ - feature-extraction
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  ---