ILKT
/

2024-06-17_21-37-12

@@ -1,199 +1,227 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+model-index:
+- name: 2024-06-17_21-37-12
+  results:
+  - dataset:
+      config: pl
+      name: MTEB MassiveIntentClassification
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 0.13691997310020174
+    task:
+      type: Classification
+  - dataset:
+      config: pl
+      name: MTEB MassiveIntentClassification
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+      split: validation
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 0.12454500737825873
+    task:
+      type: Classification
+  - dataset:
+      config: pl
+      name: MTEB MassiveScenarioClassification
+      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 0.2101882985877606
+    task:
+      type: Classification
+  - dataset:
+      config: pl
+      name: MTEB MassiveScenarioClassification
+      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
+      split: validation
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 0.2044761436301033
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB CBD
+      revision: 36ddb419bcffe6a5374c3891957912892916f28d
+      split: test
+      type: PL-MTEB/cbd
+    metrics:
+    - type: accuracy
+      value: 0.5424
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB PolEmo2.0-IN
+      revision: d90724373c70959f17d2331ad51fb60c71176b03
+      split: test
+      type: PL-MTEB/polemo2_in
+    metrics:
+    - type: accuracy
+      value: 0.3379501385041551
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB PolEmo2.0-OUT
+      revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
+      split: test
+      type: PL-MTEB/polemo2_out
+    metrics:
+    - type: accuracy
+      value: 0.2777327935222672
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AllegroReviews
+      revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
+      split: test
+      type: PL-MTEB/allegro-reviews
+    metrics:
+    - type: accuracy
+      value: 0.20725646123260436
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB PAC
+      revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
+      split: test
+      type: laugustyniak/abusive-clauses-pl
+    metrics:
+    - type: accuracy
+      value: 0.6321169997103967
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB EightTagsClustering
+      revision: 78b962b130c6690659c65abf67bf1c2f030606b6
+      split: test
+      type: PL-MTEB/8tags-clustering
+    metrics:
+    - type: v_measure
+      value: 0.05511158974160414
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SICK-E-PL
+      revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
+      split: test
+      type: PL-MTEB/sicke-pl-pairclassification
+    metrics:
+    - type: ap
+      value: 0.42361845278943716
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB CDSC-E
+      revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
+      split: test
+      type: PL-MTEB/cdsce-pairclassification
+    metrics:
+    - type: ap
+      value: 0.3709710445455343
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB PSC
+      revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
+      split: test
+      type: PL-MTEB/psc-pairclassification
+    metrics:
+    - type: ap
+      value: 0.3809467855296219
+    task:
+      type: PairClassification
+  - dataset:
+      config: pl
+      name: MTEB STS22
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_spearman
+      value: -0.08145692578014468
+    task:
+      type: STS
+  - dataset:
+      config: pl
+      name: MTEB STSBenchmarkMultilingualSTS
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: dev
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_spearman
+      value: 0.35697065008448076
+    task:
+      type: STS
+  - dataset:
+      config: pl
+      name: MTEB STSBenchmarkMultilingualSTS
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_spearman
+      value: 0.3896654714447778
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SICK-R-PL
+      revision: fd5c2441b7eeff8676768036142af4cfa42c1339
+      split: test
+      type: PL-MTEB/sickr-pl-sts
+    metrics:
+    - type: cosine_spearman
+      value: 0.3190702232991456
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB CDSC-R
+      revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
+      split: test
+      type: PL-MTEB/cdscr-sts
+    metrics:
+    - type: cosine_spearman
+      value: 0.41334466358949856
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB PlscClusteringS2S
+      revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
+      split: test
+      type: PL-MTEB/plsc-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 0.22666231671459164
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB PlscClusteringP2P
+      revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
+      split: test
+      type: PL-MTEB/plsc-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 0.18001344284415774
+    task:
+      type: Clustering
+tags:
+- mteb
 ---