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README.md
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---
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---
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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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- **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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### 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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## 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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{}
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- mteb
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language:
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- en
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- pl
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---
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model-index:
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- name: 2024-06-17_21-37-12
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results:
|
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+
- dataset:
|
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config: pl
|
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+
name: MTEB MassiveIntentClassification
|
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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: 0.13691997310020174
|
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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
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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: 0.12454500737825873
|
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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
|
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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: 0.2101882985877606
|
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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
|
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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: 0.2044761436301033
|
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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: 0.5424
|
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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-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: 0.3379501385041551
|
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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
|
89 |
+
metrics:
|
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+
- type: accuracy
|
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+
value: 0.2777327935222672
|
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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 AllegroReviews
|
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+
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
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+
split: test
|
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+
type: PL-MTEB/allegro-reviews
|
100 |
+
metrics:
|
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+
- type: accuracy
|
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+
value: 0.20725646123260436
|
103 |
+
task:
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+
type: Classification
|
105 |
+
- dataset:
|
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+
config: default
|
107 |
+
name: MTEB PAC
|
108 |
+
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
|
109 |
+
split: test
|
110 |
+
type: laugustyniak/abusive-clauses-pl
|
111 |
+
metrics:
|
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+
- type: accuracy
|
113 |
+
value: 0.6321169997103967
|
114 |
+
task:
|
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+
type: Classification
|
116 |
+
- dataset:
|
117 |
+
config: default
|
118 |
+
name: MTEB EightTagsClustering
|
119 |
+
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
|
120 |
+
split: test
|
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+
type: PL-MTEB/8tags-clustering
|
122 |
+
metrics:
|
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+
- type: v_measure
|
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+
value: 0.05511158974160414
|
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+
task:
|
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+
type: Clustering
|
127 |
+
- dataset:
|
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+
config: default
|
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+
name: MTEB SICK-E-PL
|
130 |
+
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
|
131 |
+
split: test
|
132 |
+
type: PL-MTEB/sicke-pl-pairclassification
|
133 |
+
metrics:
|
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+
- type: ap
|
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+
value: 0.42361845278943716
|
136 |
+
task:
|
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+
type: PairClassification
|
138 |
+
- dataset:
|
139 |
+
config: default
|
140 |
+
name: MTEB CDSC-E
|
141 |
+
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
|
142 |
+
split: test
|
143 |
+
type: PL-MTEB/cdsce-pairclassification
|
144 |
+
metrics:
|
145 |
+
- type: ap
|
146 |
+
value: 0.3709710445455343
|
147 |
+
task:
|
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+
type: PairClassification
|
149 |
+
- dataset:
|
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+
config: default
|
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+
name: MTEB PSC
|
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+
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
|
153 |
+
split: test
|
154 |
+
type: PL-MTEB/psc-pairclassification
|
155 |
+
metrics:
|
156 |
+
- type: ap
|
157 |
+
value: 0.3809467855296219
|
158 |
+
task:
|
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+
type: PairClassification
|
160 |
+
- dataset:
|
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+
config: pl
|
162 |
+
name: MTEB STS22
|
163 |
+
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
|
164 |
+
split: test
|
165 |
+
type: mteb/sts22-crosslingual-sts
|
166 |
+
metrics:
|
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+
- type: cosine_spearman
|
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+
value: -0.08145692578014468
|
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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
|
174 |
+
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
|
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+
split: dev
|
176 |
+
type: mteb/stsb_multi_mt
|
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+
metrics:
|
178 |
+
- type: cosine_spearman
|
179 |
+
value: 0.35697065008448076
|
180 |
+
task:
|
181 |
+
type: STS
|
182 |
+
- dataset:
|
183 |
+
config: pl
|
184 |
+
name: MTEB STSBenchmarkMultilingualSTS
|
185 |
+
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
|
186 |
+
split: test
|
187 |
+
type: mteb/stsb_multi_mt
|
188 |
+
metrics:
|
189 |
+
- type: cosine_spearman
|
190 |
+
value: 0.3896654714447778
|
191 |
+
task:
|
192 |
+
type: STS
|
193 |
+
- dataset:
|
194 |
+
config: default
|
195 |
+
name: MTEB SICK-R-PL
|
196 |
+
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
|
197 |
+
split: test
|
198 |
+
type: PL-MTEB/sickr-pl-sts
|
199 |
+
metrics:
|
200 |
+
- type: cosine_spearman
|
201 |
+
value: 0.3190702232991456
|
202 |
+
task:
|
203 |
+
type: STS
|
204 |
+
- dataset:
|
205 |
+
config: default
|
206 |
+
name: MTEB CDSC-R
|
207 |
+
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
|
208 |
+
split: test
|
209 |
+
type: PL-MTEB/cdscr-sts
|
210 |
+
metrics:
|
211 |
+
- type: cosine_spearman
|
212 |
+
value: 0.41334466358949856
|
213 |
+
task:
|
214 |
+
type: STS
|
215 |
+
- dataset:
|
216 |
+
config: default
|
217 |
+
name: MTEB PlscClusteringS2S
|
218 |
+
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
|
219 |
+
split: test
|
220 |
+
type: PL-MTEB/plsc-clustering-s2s
|
221 |
+
metrics:
|
222 |
+
- type: v_measure
|
223 |
+
value: 0.22666231671459164
|
224 |
+
task:
|
225 |
+
type: Clustering
|
226 |
+
- dataset:
|
227 |
+
config: default
|
228 |
+
name: MTEB PlscClusteringP2P
|
229 |
+
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
|
230 |
+
split: test
|
231 |
+
type: PL-MTEB/plsc-clustering-p2p
|
232 |
+
metrics:
|
233 |
+
- type: v_measure
|
234 |
+
value: 0.18001344284415774
|
235 |
+
task:
|
236 |
+
type: Clustering
|
237 |
+
tags:
|
238 |
+
- mteb
|
239 |
---
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