modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
g-assismoraes/Qwen3-4B-Base-interp-perm-alpha0.5-var-imdb
|
g-assismoraes
| 2025-08-25T23:01:42Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T22:53:38Z
|
---
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]
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756162818
|
Dejiat
| 2025-08-25T23:00:45Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T23:00:42Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Po4kaLa/gemma-product-description
|
Po4kaLa
| 2025-08-25T23:00:45Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T04:13:50Z
|
---
base_model: google/gemma-3-4b-pt
library_name: transformers
model_name: gemma-product-description
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-product-description
This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Po4kaLa/gemma-product-description", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.4.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756162664
|
Dejiat
| 2025-08-25T22:58:11Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:58:06Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nnilayy/dreamer-binary-valence-LOSO-Subject-15
|
nnilayy
| 2025-08-25T22:57:35Z
| 0
| 0
| null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-25T22:57:32Z
|
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756161094
|
mang3dd
| 2025-08-25T22:57:05Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:57:02Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756162504
|
Dejiat
| 2025-08-25T22:55:26Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:55:23Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AustineJohnBreaker/smolvla_stratch_libero_goal
|
AustineJohnBreaker
| 2025-08-25T22:55:15Z
| 0
| 0
|
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:aopolin-lv/libero_goal_no_noops_lerobot_v21",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-25T22:51:55Z
|
---
base_model: lerobot/smolvla_base
datasets: aopolin-lv/libero_goal_no_noops_lerobot_v21
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- robotics
- smolvla
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
gadertokels/blockassist-bc-tenacious_waddling_clam_1756162430
|
gadertokels
| 2025-08-25T22:54:32Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious waddling clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:54:10Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious waddling clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sweaterdog/GRaPE-mini-beta-preview-safetensors
|
Sweaterdog
| 2025-08-25T22:53:36Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-25T22:53:36Z
|
---
license: apache-2.0
---
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756162279
|
ggozzy
| 2025-08-25T22:52:37Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:52:31Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756162156
|
Dejiat
| 2025-08-25T22:49:47Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:49:41Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
enacimie/SmolLM2-135M-Instruct-Q4_K_M-GGUF
|
enacimie
| 2025-08-25T22:49:46Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"safetensors",
"onnx",
"transformers.js",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:HuggingFaceTB/SmolLM2-135M-Instruct",
"base_model:quantized:HuggingFaceTB/SmolLM2-135M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-25T22:49:42Z
|
---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- safetensors
- onnx
- transformers.js
- llama-cpp
- gguf-my-repo
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
---
# enacimie/SmolLM2-135M-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo enacimie/SmolLM2-135M-Instruct-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo enacimie/SmolLM2-135M-Instruct-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo enacimie/SmolLM2-135M-Instruct-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo enacimie/SmolLM2-135M-Instruct-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-q4_k_m.gguf -c 2048
```
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1756160580
|
rafsya427
| 2025-08-25T22:49:15Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:49:12Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fireamn/blockassist-bc-lightfooted_grassy_bison_1756160707
|
fireamn
| 2025-08-25T22:48:52Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted grassy bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:48:43Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted grassy bison
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
enacimie/SmolLM2-135M-Instruct-Q2_K-GGUF
|
enacimie
| 2025-08-25T22:48:47Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"safetensors",
"onnx",
"transformers.js",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:HuggingFaceTB/SmolLM2-135M-Instruct",
"base_model:quantized:HuggingFaceTB/SmolLM2-135M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-25T22:48:44Z
|
---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- safetensors
- onnx
- transformers.js
- llama-cpp
- gguf-my-repo
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
---
# enacimie/SmolLM2-135M-Instruct-Q2_K-GGUF
This model was converted to GGUF format from [`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo enacimie/SmolLM2-135M-Instruct-Q2_K-GGUF --hf-file smollm2-135m-instruct-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo enacimie/SmolLM2-135M-Instruct-Q2_K-GGUF --hf-file smollm2-135m-instruct-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo enacimie/SmolLM2-135M-Instruct-Q2_K-GGUF --hf-file smollm2-135m-instruct-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo enacimie/SmolLM2-135M-Instruct-Q2_K-GGUF --hf-file smollm2-135m-instruct-q2_k.gguf -c 2048
```
|
igordarma/blockassist-bc-toothy_nimble_ape_1756160726
|
igordarma
| 2025-08-25T22:48:46Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"toothy nimble ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:48:42Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- toothy nimble ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gadertokels/blockassist-bc-tenacious_waddling_clam_1756162012
|
gadertokels
| 2025-08-25T22:47:38Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious waddling clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:47:13Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious waddling clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_077
|
AnonymousCS
| 2025-08-25T22:46:54Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:45:16Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_077
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_077
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8489
- Accuracy: 0.9535
- 1-f1: 0.3111
- 1-recall: 0.2
- 1-precision: 0.7
- Balanced Acc: 0.5976
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7122 | 1.0 | 42 | 0.5482 | 0.9445 | 0.1395 | 0.0857 | 0.375 | 0.5389 |
| 0.2773 | 2.0 | 84 | 0.5533 | 0.9565 | 0.4314 | 0.3143 | 0.6875 | 0.6532 |
| 0.1058 | 3.0 | 126 | 0.4948 | 0.9130 | 0.4082 | 0.5714 | 0.3175 | 0.7517 |
| 0.0171 | 4.0 | 168 | 1.0110 | 0.9445 | 0.3934 | 0.3429 | 0.4615 | 0.6604 |
| 0.0241 | 5.0 | 210 | 1.8489 | 0.9535 | 0.3111 | 0.2 | 0.7 | 0.5976 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
tahamajs/qwen-np-v3
|
tahamajs
| 2025-08-25T22:45:12Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"arxiv:1910.09700",
"region:us"
] |
text-generation
| 2025-08-25T22:42:46Z
|
---
base_model: unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/qwen3-4b-thinking-2507-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.17.0
|
mooperyou/blockassist-bc-downy_tawny_hippo_1756161840
|
mooperyou
| 2025-08-25T22:44:36Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"downy tawny hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:44:02Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- downy tawny hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1756160245
|
aleebaster
| 2025-08-25T22:44:02Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:43:54Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vishal2325/blockassist-bc-solitary_knobby_badger_1756159501
|
vishal2325
| 2025-08-25T22:43:57Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"solitary knobby badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:43:45Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- solitary knobby badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gadertokels/blockassist-bc-tenacious_waddling_clam_1756161771
|
gadertokels
| 2025-08-25T22:43:33Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious waddling clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:43:11Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious waddling clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
augerfrecle/blockassist-bc-timid_woolly_flea_1756161759
|
augerfrecle
| 2025-08-25T22:43:30Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid woolly flea",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:43:20Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid woolly flea
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_073
|
AnonymousCS
| 2025-08-25T22:41:56Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:40:49Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_073
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_073
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9072
- Accuracy: 0.8540
- 1-f1: 0.3291
- 1-recall: 0.4062
- 1-precision: 0.2766
- Balanced Acc: 0.6518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.642 | 1.0 | 23 | 0.5356 | 0.6667 | 0.3006 | 0.8125 | 0.1844 | 0.7325 |
| 0.3981 | 2.0 | 46 | 0.5341 | 0.7410 | 0.3286 | 0.7188 | 0.2130 | 0.7310 |
| 0.3575 | 3.0 | 69 | 0.6296 | 0.6887 | 0.3067 | 0.7812 | 0.1908 | 0.7305 |
| 0.1521 | 4.0 | 92 | 0.9072 | 0.8540 | 0.3291 | 0.4062 | 0.2766 | 0.6518 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Guilherme34/Psychologist-Romanian-hf-Q4_K_M-GGUF
|
Guilherme34
| 2025-08-25T22:41:54Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"llama-3",
"llama",
"meta",
"facebook",
"unsloth",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Guilherme34/Psychologist-Romanian-hf",
"base_model:quantized:Guilherme34/Psychologist-Romanian-hf",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-25T22:41:43Z
|
---
base_model: Guilherme34/Psychologist-Romanian-hf
language:
- en
library_name: transformers
license: llama3.2
tags:
- llama-3
- llama
- meta
- facebook
- unsloth
- transformers
- llama-cpp
- gguf-my-repo
---
# Guilherme34/Psychologist-Romanian-hf-Q4_K_M-GGUF
This model was converted to GGUF format from [`Guilherme34/Psychologist-Romanian-hf`](https://huggingface.co/Guilherme34/Psychologist-Romanian-hf) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Guilherme34/Psychologist-Romanian-hf) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Guilherme34/Psychologist-Romanian-hf-Q4_K_M-GGUF --hf-file psychologist-romanian-hf-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Guilherme34/Psychologist-Romanian-hf-Q4_K_M-GGUF --hf-file psychologist-romanian-hf-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Guilherme34/Psychologist-Romanian-hf-Q4_K_M-GGUF --hf-file psychologist-romanian-hf-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Guilherme34/Psychologist-Romanian-hf-Q4_K_M-GGUF --hf-file psychologist-romanian-hf-q4_k_m.gguf -c 2048
```
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756160263
|
helmutsukocok
| 2025-08-25T22:41:47Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:41:43Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756161562
|
ggozzy
| 2025-08-25T22:40:38Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:40:32Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756161580
|
Dejiat
| 2025-08-25T22:40:07Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:40:04Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_071
|
AnonymousCS
| 2025-08-25T22:39:46Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:38:39Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_071
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_071
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3365
- Accuracy: 0.9368
- 1-f1: 0.4848
- 1-recall: 0.8421
- 1-precision: 0.3404
- Balanced Acc: 0.8912
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4657 | 1.0 | 34 | 0.2709 | 0.9517 | 0.4583 | 0.5789 | 0.3793 | 0.7721 |
| 0.0313 | 2.0 | 68 | 0.4909 | 0.9684 | 0.3704 | 0.2632 | 0.625 | 0.6287 |
| 0.0887 | 3.0 | 102 | 0.3365 | 0.9368 | 0.4848 | 0.8421 | 0.3404 | 0.8912 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
gadertokels/blockassist-bc-tenacious_waddling_clam_1756161537
|
gadertokels
| 2025-08-25T22:39:41Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious waddling clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:39:17Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious waddling clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756159742
|
elmenbillion
| 2025-08-25T22:36:37Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:36:33Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756161367
|
Dejiat
| 2025-08-25T22:36:35Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:36:29Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756160248
|
Sayemahsjn
| 2025-08-25T22:36:25Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:36:20Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raposmans/blockassist-bc-tricky_padded_jellyfish_1756161307
|
raposmans
| 2025-08-25T22:35:53Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky padded jellyfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:35:30Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky padded jellyfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756161262
|
liukevin666
| 2025-08-25T22:35:39Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:35:20Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tamewild/4b_v65_merged_e2
|
tamewild
| 2025-08-25T22:35:01Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T22:33:42Z
|
---
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]
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756161194
|
Dejiat
| 2025-08-25T22:33:39Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:33:36Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qinuoitu/blockassist-bc-bristly_striped_flamingo_1756161130
|
qinuoitu
| 2025-08-25T22:32:22Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bristly striped flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:32:11Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bristly striped flamingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_065
|
AnonymousCS
| 2025-08-25T22:31:46Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:30:41Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_065
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_065
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4193
- Accuracy: 0.9177
- 1-f1: 0.2857
- 1-recall: 0.3913
- 1-precision: 0.225
- Balanced Acc: 0.6661
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4271 | 1.0 | 35 | 0.3482 | 0.8976 | 0.3488 | 0.6522 | 0.2381 | 0.7803 |
| 0.2295 | 2.0 | 70 | 0.5803 | 0.9488 | 0.125 | 0.0870 | 0.2222 | 0.5368 |
| 0.289 | 3.0 | 105 | 0.4193 | 0.9177 | 0.2857 | 0.3913 | 0.225 | 0.6661 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
raposmans/blockassist-bc-tricky_padded_jellyfish_1756161058
|
raposmans
| 2025-08-25T22:31:45Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky padded jellyfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:31:17Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky padded jellyfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
QuantStack/InternVL3_5-2B-gguf
|
QuantStack
| 2025-08-25T22:31:25Z
| 0
| 0
| null |
[
"gguf",
"base_model:OpenGVLab/InternVL3_5-2B",
"base_model:quantized:OpenGVLab/InternVL3_5-2B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-25T22:15:13Z
|
---
license: apache-2.0
base_model:
- OpenGVLab/InternVL3_5-2B
---
This is basically a test to see if the conversion and inference in llama.cpp works fine
It seems to work though i wont add more quant sizes for now
Since this is merely a quantization of the original model the license of the original model still applies!
|
marinebark/blockassist-bc-durable_wary_alligator_1756158299
|
marinebark
| 2025-08-25T22:30:39Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable wary alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:30:26Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable wary alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756159301
|
kojeklollipop
| 2025-08-25T22:30:23Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:30:19Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756160845
|
ggozzy
| 2025-08-25T22:28:59Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:28:30Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raposmans/blockassist-bc-tricky_padded_jellyfish_1756160861
|
raposmans
| 2025-08-25T22:28:26Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky padded jellyfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:28:00Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky padded jellyfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_062
|
AnonymousCS
| 2025-08-25T22:28:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:26:48Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_062
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_062
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5853
- Accuracy: 0.9316
- 1-f1: 0.3409
- 1-recall: 0.5172
- 1-precision: 0.2542
- Balanced Acc: 0.7318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3666 | 1.0 | 53 | 0.3686 | 0.9552 | 0.3871 | 0.4138 | 0.3636 | 0.6941 |
| 0.1834 | 2.0 | 106 | 0.3433 | 0.9009 | 0.2759 | 0.5517 | 0.1839 | 0.7325 |
| 0.4415 | 3.0 | 159 | 0.4318 | 0.9292 | 0.3333 | 0.5172 | 0.2459 | 0.7305 |
| 0.0842 | 4.0 | 212 | 0.5853 | 0.9316 | 0.3409 | 0.5172 | 0.2542 | 0.7318 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756160825
|
Dejiat
| 2025-08-25T22:27:34Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:27:29Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sa7270/harm60_fin20_l9
|
sa7270
| 2025-08-25T22:27:09Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T19:29:16Z
|
---
library_name: transformers
tags:
- trl
- sft
---
# 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]
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756159257
|
mang3dd
| 2025-08-25T22:25:52Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:25:48Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_060
|
AnonymousCS
| 2025-08-25T22:25:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:24:23Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_060
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_060
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8386
- Accuracy: 0.9484
- 1-f1: 0.5909
- 1-recall: 0.4815
- 1-precision: 0.7647
- Balanced Acc: 0.7345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3607 | 1.0 | 22 | 0.1948 | 0.8768 | 0.5567 | 1.0 | 0.3857 | 0.9332 |
| 0.1164 | 2.0 | 44 | 0.2013 | 0.9398 | 0.6667 | 0.7778 | 0.5833 | 0.8656 |
| 0.0699 | 3.0 | 66 | 0.1683 | 0.9398 | 0.6957 | 0.8889 | 0.5714 | 0.9165 |
| 0.0583 | 4.0 | 88 | 0.2549 | 0.9570 | 0.7273 | 0.7407 | 0.7143 | 0.8579 |
| 0.0019 | 5.0 | 110 | 0.8386 | 0.9484 | 0.5909 | 0.4815 | 0.7647 | 0.7345 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
tejas509/gpt-oss-20b-multilingual-reasoner
|
tejas509
| 2025-08-25T22:25:34Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dataset:tejas509/Multilingual-Thinking",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T22:06:14Z
|
---
base_model: openai/gpt-oss-20b
datasets: tejas509/Multilingual-Thinking
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [tejas509/Multilingual-Thinking](https://huggingface.co/datasets/tejas509/Multilingual-Thinking) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tejas509/gpt-oss-20b-multilingual-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
raposmans/blockassist-bc-tricky_padded_jellyfish_1756160650
|
raposmans
| 2025-08-25T22:24:53Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky padded jellyfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:24:30Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky padded jellyfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
motza0025/blockassist-bc-ferocious_territorial_chinchilla_1756159183
|
motza0025
| 2025-08-25T22:24:49Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ferocious territorial chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:24:35Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ferocious territorial chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thoddnn/multilingual-e5-small-4bit-mlx
|
thoddnn
| 2025-08-25T22:24:34Z
| 0
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"mlx",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-25T21:59:01Z
|
---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
- mlx
model-index:
- name: intfloat/multilingual-e5-small
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 36.9996434842022
- type: f1
value: 67.95453679103099
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.64882226980728
- type: ap
value: 82.11942130026586
- type: f1
value: 69.87963421606715
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.8095952023988
- type: ap
value: 24.46869495579561
- type: f1
value: 63.00108480037597
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.186295503212
- type: ap
value: 15.496804690197042
- type: f1
value: 52.07153895475031
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.699325
- type: ap
value: 85.27039559917269
- type: f1
value: 88.65556295032513
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.69799999999999
- type: f1
value: 43.73187348654165
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.245999999999995
- type: f1
value: 39.3863530637684
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.394
- type: f1
value: 39.301223469483446
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.864
- type: f1
value: 37.97974261868003
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.682
- type: f1
value: 37.07399369768313
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.504
- type: f1
value: 36.62317273874278
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.061
- type: map_at_10
value: 31.703
- type: map_at_100
value: 32.967
- type: map_at_1000
value: 33.001000000000005
- type: map_at_3
value: 27.466
- type: map_at_5
value: 29.564
- type: mrr_at_1
value: 19.559
- type: mrr_at_10
value: 31.874999999999996
- type: mrr_at_100
value: 33.146
- type: mrr_at_1000
value: 33.18
- type: mrr_at_3
value: 27.667
- type: mrr_at_5
value: 29.74
- type: ndcg_at_1
value: 19.061
- type: ndcg_at_10
value: 39.062999999999995
- type: ndcg_at_100
value: 45.184000000000005
- type: ndcg_at_1000
value: 46.115
- type: ndcg_at_3
value: 30.203000000000003
- type: ndcg_at_5
value: 33.953
- type: precision_at_1
value: 19.061
- type: precision_at_10
value: 6.279999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 12.706999999999999
- type: precision_at_5
value: 9.431000000000001
- type: recall_at_1
value: 19.061
- type: recall_at_10
value: 62.802
- type: recall_at_100
value: 91.323
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 38.122
- type: recall_at_5
value: 47.155
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.22266660528253
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 30.79980849482483
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.8790068352054
- type: mrr
value: 71.78791276436706
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.36328364043163
- type: cos_sim_spearman
value: 82.26211536195868
- type: euclidean_pearson
value: 80.3183865039173
- type: euclidean_spearman
value: 79.88495276296132
- type: manhattan_pearson
value: 80.14484480692127
- type: manhattan_spearman
value: 80.39279565980743
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0375782881002
- type: f1
value: 97.86012526096033
- type: precision
value: 97.77139874739039
- type: recall
value: 98.0375782881002
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 93.35241030156286
- type: f1
value: 92.66050333846944
- type: precision
value: 92.3306919069631
- type: recall
value: 93.35241030156286
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 94.0699688257707
- type: f1
value: 93.50236693222492
- type: precision
value: 93.22791825424315
- type: recall
value: 94.0699688257707
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 89.25750394944708
- type: f1
value: 88.79234684921889
- type: precision
value: 88.57293312269616
- type: recall
value: 89.25750394944708
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 79.41558441558442
- type: f1
value: 79.25886487487219
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.747820820329736
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.045143830596146
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.252999999999997
- type: map_at_10
value: 31.655916666666666
- type: map_at_100
value: 32.680749999999996
- type: map_at_1000
value: 32.79483333333334
- type: map_at_3
value: 29.43691666666666
- type: map_at_5
value: 30.717416666666665
- type: mrr_at_1
value: 28.602750000000004
- type: mrr_at_10
value: 35.56875
- type: mrr_at_100
value: 36.3595
- type: mrr_at_1000
value: 36.427749999999996
- type: mrr_at_3
value: 33.586166666666664
- type: mrr_at_5
value: 34.73641666666666
- type: ndcg_at_1
value: 28.602750000000004
- type: ndcg_at_10
value: 36.06933333333334
- type: ndcg_at_100
value: 40.70141666666667
- type: ndcg_at_1000
value: 43.24341666666667
- type: ndcg_at_3
value: 32.307916666666664
- type: ndcg_at_5
value: 34.129999999999995
- type: precision_at_1
value: 28.602750000000004
- type: precision_at_10
value: 6.097666666666667
- type: precision_at_100
value: 0.9809166666666668
- type: precision_at_1000
value: 0.13766666666666663
- type: precision_at_3
value: 14.628166666666667
- type: precision_at_5
value: 10.266916666666667
- type: recall_at_1
value: 24.252999999999997
- type: recall_at_10
value: 45.31916666666667
- type: recall_at_100
value: 66.03575000000001
- type: recall_at_1000
value: 83.94708333333334
- type: recall_at_3
value: 34.71941666666666
- type: recall_at_5
value: 39.46358333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.024000000000001
- type: map_at_10
value: 15.644
- type: map_at_100
value: 17.154
- type: map_at_1000
value: 17.345
- type: map_at_3
value: 13.028
- type: map_at_5
value: 14.251
- type: mrr_at_1
value: 19.674
- type: mrr_at_10
value: 29.826999999999998
- type: mrr_at_100
value: 30.935000000000002
- type: mrr_at_1000
value: 30.987
- type: mrr_at_3
value: 26.645000000000003
- type: mrr_at_5
value: 28.29
- type: ndcg_at_1
value: 19.674
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 29.207
- type: ndcg_at_1000
value: 32.912
- type: ndcg_at_3
value: 17.952
- type: ndcg_at_5
value: 19.363
- type: precision_at_1
value: 19.674
- type: precision_at_10
value: 7.212000000000001
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.397
- type: recall_at_1
value: 9.024000000000001
- type: recall_at_10
value: 28.077999999999996
- type: recall_at_100
value: 51.403
- type: recall_at_1000
value: 72.406
- type: recall_at_3
value: 16.768
- type: recall_at_5
value: 20.737
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.012
- type: map_at_10
value: 17.138
- type: map_at_100
value: 24.146
- type: map_at_1000
value: 25.622
- type: map_at_3
value: 12.552
- type: map_at_5
value: 14.435
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 71.186
- type: mrr_at_100
value: 71.504
- type: mrr_at_1000
value: 71.514
- type: mrr_at_3
value: 69.333
- type: mrr_at_5
value: 70.408
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 37.76
- type: ndcg_at_100
value: 42.071
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 41.644
- type: ndcg_at_5
value: 39.812999999999995
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 30.15
- type: precision_at_100
value: 9.753
- type: precision_at_1000
value: 1.9189999999999998
- type: precision_at_3
value: 45.667
- type: precision_at_5
value: 39.15
- type: recall_at_1
value: 8.012
- type: recall_at_10
value: 22.599
- type: recall_at_100
value: 48.068
- type: recall_at_1000
value: 71.328
- type: recall_at_3
value: 14.043
- type: recall_at_5
value: 17.124
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.455
- type: f1
value: 37.59462649781862
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.092
- type: map_at_10
value: 69.586
- type: map_at_100
value: 69.968
- type: map_at_1000
value: 69.982
- type: map_at_3
value: 67.48100000000001
- type: map_at_5
value: 68.915
- type: mrr_at_1
value: 62.166
- type: mrr_at_10
value: 73.588
- type: mrr_at_100
value: 73.86399999999999
- type: mrr_at_1000
value: 73.868
- type: mrr_at_3
value: 71.6
- type: mrr_at_5
value: 72.99
- type: ndcg_at_1
value: 62.166
- type: ndcg_at_10
value: 75.27199999999999
- type: ndcg_at_100
value: 76.816
- type: ndcg_at_1000
value: 77.09700000000001
- type: ndcg_at_3
value: 71.36
- type: ndcg_at_5
value: 73.785
- type: precision_at_1
value: 62.166
- type: precision_at_10
value: 9.716
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.343999999999998
- type: recall_at_1
value: 58.092
- type: recall_at_10
value: 88.73400000000001
- type: recall_at_100
value: 95.195
- type: recall_at_1000
value: 97.04599999999999
- type: recall_at_3
value: 78.45
- type: recall_at_5
value: 84.316
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.649
- type: map_at_10
value: 26.457000000000004
- type: map_at_100
value: 28.169
- type: map_at_1000
value: 28.352
- type: map_at_3
value: 23.305
- type: map_at_5
value: 25.169000000000004
- type: mrr_at_1
value: 32.407000000000004
- type: mrr_at_10
value: 40.922
- type: mrr_at_100
value: 41.931000000000004
- type: mrr_at_1000
value: 41.983
- type: mrr_at_3
value: 38.786
- type: mrr_at_5
value: 40.205999999999996
- type: ndcg_at_1
value: 32.407000000000004
- type: ndcg_at_10
value: 33.314
- type: ndcg_at_100
value: 40.312
- type: ndcg_at_1000
value: 43.685
- type: ndcg_at_3
value: 30.391000000000002
- type: ndcg_at_5
value: 31.525
- type: precision_at_1
value: 32.407000000000004
- type: precision_at_10
value: 8.966000000000001
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 14.722
- type: recall_at_1
value: 16.649
- type: recall_at_10
value: 39.117000000000004
- type: recall_at_100
value: 65.726
- type: recall_at_1000
value: 85.784
- type: recall_at_3
value: 27.914
- type: recall_at_5
value: 33.289
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.253
- type: map_at_10
value: 56.16799999999999
- type: map_at_100
value: 57.06099999999999
- type: map_at_1000
value: 57.126
- type: map_at_3
value: 52.644999999999996
- type: map_at_5
value: 54.909
- type: mrr_at_1
value: 72.505
- type: mrr_at_10
value: 79.66
- type: mrr_at_100
value: 79.869
- type: mrr_at_1000
value: 79.88
- type: mrr_at_3
value: 78.411
- type: mrr_at_5
value: 79.19800000000001
- type: ndcg_at_1
value: 72.505
- type: ndcg_at_10
value: 65.094
- type: ndcg_at_100
value: 68.219
- type: ndcg_at_1000
value: 69.515
- type: ndcg_at_3
value: 59.99
- type: ndcg_at_5
value: 62.909000000000006
- type: precision_at_1
value: 72.505
- type: precision_at_10
value: 13.749
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.357
- type: precision_at_5
value: 25.313000000000002
- type: recall_at_1
value: 36.253
- type: recall_at_10
value: 68.744
- type: recall_at_100
value: 80.925
- type: recall_at_1000
value: 89.534
- type: recall_at_3
value: 57.535000000000004
- type: recall_at_5
value: 63.282000000000004
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.82239999999999
- type: ap
value: 75.65895781725314
- type: f1
value: 80.75880969095746
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 34.075
- type: map_at_100
value: 35.229
- type: map_at_1000
value: 35.276999999999994
- type: map_at_3
value: 30.245
- type: map_at_5
value: 32.42
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 34.638000000000005
- type: mrr_at_100
value: 35.744
- type: mrr_at_1000
value: 35.787
- type: mrr_at_3
value: 30.891000000000002
- type: mrr_at_5
value: 33.042
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 40.991
- type: ndcg_at_100
value: 46.563
- type: ndcg_at_1000
value: 47.743
- type: ndcg_at_3
value: 33.198
- type: ndcg_at_5
value: 37.069
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.216999999999999
- type: precision_at_5
value: 10.487
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 62.303
- type: recall_at_100
value: 88.124
- type: recall_at_1000
value: 97.08
- type: recall_at_3
value: 41.099999999999994
- type: recall_at_5
value: 50.381
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.06703146374831
- type: f1
value: 90.86867815863172
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.46970977740209
- type: f1
value: 86.36832872036588
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.26951300867245
- type: f1
value: 88.93561193959502
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 84.22799874725963
- type: f1
value: 84.30490069236556
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.02007888131948
- type: f1
value: 85.39376041027991
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.34900542495481
- type: f1
value: 85.39859673336713
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.078431372549
- type: f1
value: 53.45071102002276
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.85798816568047
- type: f1
value: 46.53112748993529
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.96864576384256
- type: f1
value: 45.966703022829506
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 61.31537738803633
- type: f1
value: 45.52601712835461
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 66.29616349946218
- type: f1
value: 47.24166485726613
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.51537070524412
- type: f1
value: 49.463476319014276
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.06792199058508
- type: f1
value: 54.094921857502285
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 51.960322797579025
- type: f1
value: 48.547371223370945
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 54.425016812373904
- type: f1
value: 50.47069202054312
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.798251513113655
- type: f1
value: 57.05013069086648
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.37794216543376
- type: f1
value: 56.3607992649805
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 46.56018829858777
- type: f1
value: 43.87319715715134
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.9724277067922
- type: f1
value: 59.36480066245562
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.72696704774715
- type: f1
value: 59.143595966615855
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 61.5971755211836
- type: f1
value: 59.169445724946726
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.29589778076665
- type: f1
value: 67.7577001808977
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.31136516476126
- type: f1
value: 64.52032955983242
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.54472091459314
- type: f1
value: 61.47903120066317
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 61.45595158036314
- type: f1
value: 58.0891846024637
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.47074646940149
- type: f1
value: 62.84830858877575
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (he)
type: mteb/amazon_massive_intent
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 58.046402151983855
- type: f1
value: 55.269074430533195
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hi)
type: mteb/amazon_massive_intent
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.06523201075991
- type: f1
value: 61.35339643021369
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hu)
type: mteb/amazon_massive_intent
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.954942837928726
- type: f1
value: 57.07035922704846
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hy)
type: mteb/amazon_massive_intent
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.404169468728995
- type: f1
value: 53.94259011839138
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (id)
type: mteb/amazon_massive_intent
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.16610625420309
- type: f1
value: 61.337103431499365
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (is)
type: mteb/amazon_massive_intent
config: is
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 52.262945527908535
- type: f1
value: 49.7610691598921
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (it)
type: mteb/amazon_massive_intent
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.54472091459314
- type: f1
value: 63.469099018440154
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ja)
type: mteb/amazon_massive_intent
config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.22797579018157
- type: f1
value: 64.89098471083001
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (jv)
type: mteb/amazon_massive_intent
config: jv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 50.847343644922674
- type: f1
value: 47.8536963168393
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ka)
type: mteb/amazon_massive_intent
config: ka
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 48.45326160053799
- type: f1
value: 46.370078045805556
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (km)
type: mteb/amazon_massive_intent
config: km
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 42.83120376597175
- type: f1
value: 39.68948521599982
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (kn)
type: mteb/amazon_massive_intent
config: kn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.5084061869536
- type: f1
value: 53.961876160401545
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ko)
type: mteb/amazon_massive_intent
config: ko
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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metrics:
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value: 70.89441829186282
- type: f1
value: 70.03064076194089
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.15063887020847
- type: f1
value: 56.23326278499678
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.43846671149966
- type: f1
value: 57.70440450281974
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8507061197041
- type: f1
value: 59.22916396061171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.65568258238063
- type: f1
value: 69.90736239440633
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8843308675185
- type: f1
value: 59.30332663713599
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.05312710154674
- type: f1
value: 67.44024062594775
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.111634162743776
- type: f1
value: 60.89083013084519
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.44115669132482
- type: f1
value: 67.92227541674552
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.4687289845326
- type: f1
value: 74.16376793486025
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.31876260928043
- type: f1
value: 68.5246745215607
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.90431696479766
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.259158476693774
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.28445330838555
- type: mrr
value: 31.15758529581164
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.353
- type: map_at_10
value: 11.565
- type: map_at_100
value: 14.097000000000001
- type: map_at_1000
value: 15.354999999999999
- type: map_at_3
value: 8.749
- type: map_at_5
value: 9.974
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 50.589
- type: mrr_at_100
value: 51.187000000000005
- type: mrr_at_1000
value: 51.233
- type: mrr_at_3
value: 48.246
- type: mrr_at_5
value: 49.546
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 31.009999999999998
- type: ndcg_at_100
value: 28.026
- type: ndcg_at_1000
value: 36.905
- type: ndcg_at_3
value: 35.983
- type: ndcg_at_5
value: 33.764
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 22.786
- type: precision_at_100
value: 6.916
- type: precision_at_1000
value: 1.981
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 28.731
- type: recall_at_1
value: 5.353
- type: recall_at_10
value: 15.039
- type: recall_at_100
value: 27.348
- type: recall_at_1000
value: 59.453
- type: recall_at_3
value: 9.792
- type: recall_at_5
value: 11.882
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.852
- type: map_at_10
value: 48.924
- type: map_at_100
value: 49.854
- type: map_at_1000
value: 49.886
- type: map_at_3
value: 44.9
- type: map_at_5
value: 47.387
- type: mrr_at_1
value: 38.035999999999994
- type: mrr_at_10
value: 51.644
- type: mrr_at_100
value: 52.339
- type: mrr_at_1000
value: 52.35999999999999
- type: mrr_at_3
value: 48.421
- type: mrr_at_5
value: 50.468999999999994
- type: ndcg_at_1
value: 38.007000000000005
- type: ndcg_at_10
value: 56.293000000000006
- type: ndcg_at_100
value: 60.167
- type: ndcg_at_1000
value: 60.916000000000004
- type: ndcg_at_3
value: 48.903999999999996
- type: ndcg_at_5
value: 52.978
- type: precision_at_1
value: 38.007000000000005
- type: precision_at_10
value: 9.041
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.084
- type: precision_at_5
value: 15.608
- type: recall_at_1
value: 33.852
- type: recall_at_10
value: 75.893
- type: recall_at_100
value: 92.589
- type: recall_at_1000
value: 98.153
- type: recall_at_3
value: 56.969
- type: recall_at_5
value: 66.283
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.174
- type: map_at_10
value: 82.891
- type: map_at_100
value: 83.545
- type: map_at_1000
value: 83.56700000000001
- type: map_at_3
value: 79.944
- type: map_at_5
value: 81.812
- type: mrr_at_1
value: 79.67999999999999
- type: mrr_at_10
value: 86.279
- type: mrr_at_100
value: 86.39
- type: mrr_at_1000
value: 86.392
- type: mrr_at_3
value: 85.21
- type: mrr_at_5
value: 85.92999999999999
- type: ndcg_at_1
value: 79.69000000000001
- type: ndcg_at_10
value: 86.929
- type: ndcg_at_100
value: 88.266
- type: ndcg_at_1000
value: 88.428
- type: ndcg_at_3
value: 83.899
- type: ndcg_at_5
value: 85.56700000000001
- type: precision_at_1
value: 79.69000000000001
- type: precision_at_10
value: 13.161000000000001
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.603
- type: precision_at_5
value: 24.138
- type: recall_at_1
value: 69.174
- type: recall_at_10
value: 94.529
- type: recall_at_100
value: 99.15
- type: recall_at_1000
value: 99.925
- type: recall_at_3
value: 85.86200000000001
- type: recall_at_5
value: 90.501
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 39.13064340585255
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 58.97884249325877
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.4680000000000004
- type: map_at_10
value: 7.865
- type: map_at_100
value: 9.332
- type: map_at_1000
value: 9.587
- type: map_at_3
value: 5.800000000000001
- type: map_at_5
value: 6.8790000000000004
- type: mrr_at_1
value: 17.0
- type: mrr_at_10
value: 25.629
- type: mrr_at_100
value: 26.806
- type: mrr_at_1000
value: 26.889000000000003
- type: mrr_at_3
value: 22.8
- type: mrr_at_5
value: 24.26
- type: ndcg_at_1
value: 17.0
- type: ndcg_at_10
value: 13.895
- type: ndcg_at_100
value: 20.491999999999997
- type: ndcg_at_1000
value: 25.759999999999998
- type: ndcg_at_3
value: 13.347999999999999
- type: ndcg_at_5
value: 11.61
- type: precision_at_1
value: 17.0
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 1.669
- type: precision_at_1000
value: 0.294
- type: precision_at_3
value: 12.3
- type: precision_at_5
value: 10.02
- type: recall_at_1
value: 3.4680000000000004
- type: recall_at_10
value: 14.363000000000001
- type: recall_at_100
value: 33.875
- type: recall_at_1000
value: 59.711999999999996
- type: recall_at_3
value: 7.483
- type: recall_at_5
value: 10.173
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.04084311714061
- type: cos_sim_spearman
value: 77.51342467443078
- type: euclidean_pearson
value: 80.0321166028479
- type: euclidean_spearman
value: 77.29249114733226
- type: manhattan_pearson
value: 80.03105964262431
- type: manhattan_spearman
value: 77.22373689514794
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1680158034387
- type: cos_sim_spearman
value: 76.55983344071117
- type: euclidean_pearson
value: 79.75266678300143
- type: euclidean_spearman
value: 75.34516823467025
- type: manhattan_pearson
value: 79.75959151517357
- type: manhattan_spearman
value: 75.42330344141912
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 76.48898993209346
- type: cos_sim_spearman
value: 76.96954120323366
- type: euclidean_pearson
value: 76.94139109279668
- type: euclidean_spearman
value: 76.85860283201711
- type: manhattan_pearson
value: 76.6944095091912
- type: manhattan_spearman
value: 76.61096912972553
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 77.85082366246944
- type: cos_sim_spearman
value: 75.52053350101731
- type: euclidean_pearson
value: 77.1165845070926
- type: euclidean_spearman
value: 75.31216065884388
- type: manhattan_pearson
value: 77.06193941833494
- type: manhattan_spearman
value: 75.31003701700112
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.36305246526497
- type: cos_sim_spearman
value: 87.11704613927415
- type: euclidean_pearson
value: 86.04199125810939
- type: euclidean_spearman
value: 86.51117572414263
- type: manhattan_pearson
value: 86.0805106816633
- type: manhattan_spearman
value: 86.52798366512229
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.18536255599724
- type: cos_sim_spearman
value: 83.63377151025418
- type: euclidean_pearson
value: 83.24657467993141
- type: euclidean_spearman
value: 84.02751481993825
- type: manhattan_pearson
value: 83.11941806582371
- type: manhattan_spearman
value: 83.84251281019304
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.95816528475514
- type: cos_sim_spearman
value: 78.86607380120462
- type: euclidean_pearson
value: 78.51268699230545
- type: euclidean_spearman
value: 79.11649316502229
- type: manhattan_pearson
value: 78.32367302808157
- type: manhattan_spearman
value: 78.90277699624637
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.89126914997624
- type: cos_sim_spearman
value: 73.0296921832678
- type: euclidean_pearson
value: 71.50385903677738
- type: euclidean_spearman
value: 73.13368899716289
- type: manhattan_pearson
value: 71.47421463379519
- type: manhattan_spearman
value: 73.03383242946575
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 59.22923684492637
- type: cos_sim_spearman
value: 57.41013211368396
- type: euclidean_pearson
value: 61.21107388080905
- type: euclidean_spearman
value: 60.07620768697254
- type: manhattan_pearson
value: 59.60157142786555
- type: manhattan_spearman
value: 59.14069604103739
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.24345978774299
- type: cos_sim_spearman
value: 77.24225743830719
- type: euclidean_pearson
value: 76.66226095469165
- type: euclidean_spearman
value: 77.60708820493146
- type: manhattan_pearson
value: 76.05303324760429
- type: manhattan_spearman
value: 76.96353149912348
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.50879160160852
- type: cos_sim_spearman
value: 86.43594662965224
- type: euclidean_pearson
value: 86.06846012826577
- type: euclidean_spearman
value: 86.02041395794136
- type: manhattan_pearson
value: 86.10916255616904
- type: manhattan_spearman
value: 86.07346068198953
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 58.39803698977196
- type: cos_sim_spearman
value: 55.96910950423142
- type: euclidean_pearson
value: 58.17941175613059
- type: euclidean_spearman
value: 55.03019330522745
- type: manhattan_pearson
value: 57.333358138183286
- type: manhattan_spearman
value: 54.04614023149965
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 70.98304089637197
- type: cos_sim_spearman
value: 72.44071656215888
- type: euclidean_pearson
value: 72.19224359033983
- type: euclidean_spearman
value: 73.89871188913025
- type: manhattan_pearson
value: 71.21098311547406
- type: manhattan_spearman
value: 72.93405764824821
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.99792397466308
- type: cos_sim_spearman
value: 84.83824377879495
- type: euclidean_pearson
value: 85.70043288694438
- type: euclidean_spearman
value: 84.70627558703686
- type: manhattan_pearson
value: 85.89570850150801
- type: manhattan_spearman
value: 84.95806105313007
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.21850322994712
- type: cos_sim_spearman
value: 72.28669398117248
- type: euclidean_pearson
value: 73.40082510412948
- type: euclidean_spearman
value: 73.0326539281865
- type: manhattan_pearson
value: 71.8659633964841
- type: manhattan_spearman
value: 71.57817425823303
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.80921368595645
- type: cos_sim_spearman
value: 77.33209091229315
- type: euclidean_pearson
value: 76.53159540154829
- type: euclidean_spearman
value: 78.17960842810093
- type: manhattan_pearson
value: 76.13530186637601
- type: manhattan_spearman
value: 78.00701437666875
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.74980608267349
- type: cos_sim_spearman
value: 75.37597374318821
- type: euclidean_pearson
value: 74.90506081911661
- type: euclidean_spearman
value: 75.30151613124521
- type: manhattan_pearson
value: 74.62642745918002
- type: manhattan_spearman
value: 75.18619716592303
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.632662289205584
- type: cos_sim_spearman
value: 60.938543391610914
- type: euclidean_pearson
value: 62.113200529767056
- type: euclidean_spearman
value: 61.410312633261164
- type: manhattan_pearson
value: 61.75494698945686
- type: manhattan_spearman
value: 60.92726195322362
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.283470551557244
- type: cos_sim_spearman
value: 53.44833015864201
- type: euclidean_pearson
value: 41.17892011120893
- type: euclidean_spearman
value: 53.81441383126767
- type: manhattan_pearson
value: 41.17482200420659
- type: manhattan_spearman
value: 53.82180269276363
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.5069165306236
- type: cos_sim_spearman
value: 66.87803259033826
- type: euclidean_pearson
value: 63.5428979418236
- type: euclidean_spearman
value: 66.9293576586897
- type: manhattan_pearson
value: 63.59789526178922
- type: manhattan_spearman
value: 66.86555009875066
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.23026196280264
- type: cos_sim_spearman
value: 35.79397812652861
- type: euclidean_pearson
value: 17.828102102767353
- type: euclidean_spearman
value: 35.721501145568894
- type: manhattan_pearson
value: 17.77134274219677
- type: manhattan_spearman
value: 35.98107902846267
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.51946541393812
- type: cos_sim_spearman
value: 63.714686006214485
- type: euclidean_pearson
value: 58.32104651305898
- type: euclidean_spearman
value: 62.237110895702216
- type: manhattan_pearson
value: 58.579416468759185
- type: manhattan_spearman
value: 62.459738981727
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.76009839569795
- type: cos_sim_spearman
value: 56.65188431953149
- type: euclidean_pearson
value: 50.997682160915595
- type: euclidean_spearman
value: 55.99910008818135
- type: manhattan_pearson
value: 50.76220659606342
- type: manhattan_spearman
value: 55.517347595391456
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.717524559088005
- type: cos_sim_spearman
value: 66.83570886252286
- type: euclidean_pearson
value: 58.41338625505467
- type: euclidean_spearman
value: 66.68991427704938
- type: manhattan_pearson
value: 58.78638572916807
- type: manhattan_spearman
value: 66.58684161046335
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.2962042954962
- type: cos_sim_spearman
value: 76.58255504852025
- type: euclidean_pearson
value: 75.70983192778257
- type: euclidean_spearman
value: 77.4547684870542
- type: manhattan_pearson
value: 75.75565853870485
- type: manhattan_spearman
value: 76.90208974949428
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.47396266924846
- type: cos_sim_spearman
value: 56.492267162048606
- type: euclidean_pearson
value: 55.998505203070195
- type: euclidean_spearman
value: 56.46447012960222
- type: manhattan_pearson
value: 54.873172394430995
- type: manhattan_spearman
value: 56.58111534551218
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.87177267688686
- type: cos_sim_spearman
value: 74.57160943395763
- type: euclidean_pearson
value: 70.88330406826788
- type: euclidean_spearman
value: 74.29767636038422
- type: manhattan_pearson
value: 71.38245248369536
- type: manhattan_spearman
value: 74.53102232732175
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.80225656959544
- type: cos_sim_spearman
value: 76.52646173725735
- type: euclidean_pearson
value: 73.95710720200799
- type: euclidean_spearman
value: 76.54040031984111
- type: manhattan_pearson
value: 73.89679971946774
- type: manhattan_spearman
value: 76.60886958161574
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.70844249898789
- type: cos_sim_spearman
value: 72.68571783670241
- type: euclidean_pearson
value: 72.38800772441031
- type: euclidean_spearman
value: 72.86804422703312
- type: manhattan_pearson
value: 71.29840508203515
- type: manhattan_spearman
value: 71.86264441749513
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.647478923935694
- type: cos_sim_spearman
value: 63.74453623540931
- type: euclidean_pearson
value: 59.60138032437505
- type: euclidean_spearman
value: 63.947930832166065
- type: manhattan_pearson
value: 58.59735509491861
- type: manhattan_spearman
value: 62.082503844627404
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.8722516867162
- type: cos_sim_spearman
value: 71.81208592523012
- type: euclidean_pearson
value: 67.95315252165956
- type: euclidean_spearman
value: 73.00749822046009
- type: manhattan_pearson
value: 68.07884688638924
- type: manhattan_spearman
value: 72.34210325803069
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.5405814240949
- type: cos_sim_spearman
value: 60.56838649023775
- type: euclidean_pearson
value: 53.011731611314104
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 53.623067729338494
- type: manhattan_spearman
value: 58.018756154446926
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 13.611046866216112
- type: cos_sim_spearman
value: 28.238192909158492
- type: euclidean_pearson
value: 22.16189199885129
- type: euclidean_spearman
value: 35.012895679076564
- type: manhattan_pearson
value: 21.969771178698387
- type: manhattan_spearman
value: 32.456985088607475
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 74.58077407011655
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 74.64613843596234
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 75.15335973101396
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.0739825531578
- type: cos_sim_spearman
value: 84.01057479311115
- type: euclidean_pearson
value: 83.85453227433344
- type: euclidean_spearman
value: 84.01630226898655
- type: manhattan_pearson
value: 83.75323603028978
- type: manhattan_spearman
value: 83.89677983727685
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.12945623123957
- type: mrr
value: 93.87738713719106
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.983000000000004
- type: map_at_10
value: 62.946000000000005
- type: map_at_100
value: 63.514
- type: map_at_1000
value: 63.554
- type: map_at_3
value: 60.183
- type: map_at_5
value: 61.672000000000004
- type: mrr_at_1
value: 55.667
- type: mrr_at_10
value: 64.522
- type: mrr_at_100
value: 64.957
- type: mrr_at_1000
value: 64.995
- type: mrr_at_3
value: 62.388999999999996
- type: mrr_at_5
value: 63.639
- type: ndcg_at_1
value: 55.667
- type: ndcg_at_10
value: 67.704
- type: ndcg_at_100
value: 70.299
- type: ndcg_at_1000
value: 71.241
- type: ndcg_at_3
value: 62.866
- type: ndcg_at_5
value: 65.16999999999999
- type: precision_at_1
value: 55.667
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.133
- type: recall_at_1
value: 52.983000000000004
- type: recall_at_10
value: 80.656
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 67.744
- type: recall_at_5
value: 73.433
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72772277227723
- type: cos_sim_ap
value: 92.17845897992215
- type: cos_sim_f1
value: 85.9746835443038
- type: cos_sim_precision
value: 87.07692307692308
- type: cos_sim_recall
value: 84.89999999999999
- type: dot_accuracy
value: 99.3039603960396
- type: dot_ap
value: 60.70244020124878
- type: dot_f1
value: 59.92742353551063
- type: dot_precision
value: 62.21743810548978
- type: dot_recall
value: 57.8
- type: euclidean_accuracy
value: 99.71683168316832
- type: euclidean_ap
value: 91.53997039964659
- type: euclidean_f1
value: 84.88372093023257
- type: euclidean_precision
value: 90.02242152466367
- type: euclidean_recall
value: 80.30000000000001
- type: manhattan_accuracy
value: 99.72376237623763
- type: manhattan_ap
value: 91.80756777790289
- type: manhattan_f1
value: 85.48468106479157
- type: manhattan_precision
value: 85.8728557013118
- type: manhattan_recall
value: 85.1
- type: max_accuracy
value: 99.72772277227723
- type: max_ap
value: 92.17845897992215
- type: max_f1
value: 85.9746835443038
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.52464042600003
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.071631948736
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.19552407604654
- type: mrr
value: 49.95269130379425
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.345293033095427
- type: cos_sim_spearman
value: 29.976931423258403
- type: dot_pearson
value: 27.047078008958408
- type: dot_spearman
value: 27.75894368380218
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.706
- type: map_at_100
value: 9.634
- type: map_at_1000
value: 23.665
- type: map_at_3
value: 0.5950000000000001
- type: map_at_5
value: 0.95
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 80.0
- type: ndcg_at_10
value: 72.573
- type: ndcg_at_100
value: 53.954
- type: ndcg_at_1000
value: 47.760999999999996
- type: ndcg_at_3
value: 76.173
- type: ndcg_at_5
value: 75.264
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 76.4
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.802
- type: precision_at_3
value: 81.333
- type: precision_at_5
value: 80.4
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 1.925
- type: recall_at_100
value: 12.762
- type: recall_at_1000
value: 44.946000000000005
- type: recall_at_3
value: 0.634
- type: recall_at_5
value: 1.051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.0
- type: f1
value: 88.55666666666666
- type: precision
value: 87.46166666666667
- type: recall
value: 91.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.22543352601156
- type: f1
value: 51.03220478943021
- type: precision
value: 48.8150289017341
- type: recall
value: 57.22543352601156
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.58536585365854
- type: f1
value: 39.66870798578116
- type: precision
value: 37.416085946573745
- type: recall
value: 46.58536585365854
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.77999999999999
- type: precision
value: 85.45333333333332
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.58333333333331
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.3
- type: precision
value: 89.31666666666668
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.67190476190476
- type: precision
value: 82.23333333333332
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.23229092632078
- type: precision
value: 39.851634683724235
- type: recall
value: 50.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.3
- type: f1
value: 70.86190476190477
- type: precision
value: 68.68777777777777
- type: recall
value: 76.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.073170731707314
- type: f1
value: 50.658958927251604
- type: precision
value: 48.26480836236933
- type: recall
value: 57.073170731707314
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 68.2
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value: 68.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.52126366950182
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value: 70.92171498003819
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value: 77.52126366950182
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type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.32422360248447
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value: 63.063067367415194
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value: 70.78260869565217
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type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 78.43478260869566
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value: 70.63768115942028
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value: 78.43478260869566
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type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 60.9
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value: 53.130476190476195
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value: 60.9
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type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 72.89999999999999
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value: 67.92023809523809
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value: 65.82595238095237
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value: 72.89999999999999
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type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 46.80337756332931
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value: 36.97101116280851
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value: 46.80337756332931
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type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.8
- type: f1
value: 86.79
- type: precision
value: 85.375
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value: 89.8
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type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 37.561071428571424
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value: 47.199999999999996
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type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.8
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value: 84.68190476190475
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value: 83.275
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value: 87.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 48.76190476190476
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value: 42.14965986394558
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value: 39.96743626743626
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value: 48.76190476190476
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type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 66.10000000000001
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value: 57.150238095238095
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value: 66.10000000000001
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type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.3
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value: 84.0
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value: 82.48666666666666
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value: 87.3
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type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.4
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value: 87.79523809523809
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value: 86.6
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value: 90.4
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type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.0
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value: 83.81
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value: 82.36666666666666
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value: 87.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 63.9
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value: 57.76533189033189
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value: 55.50595238095239
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value: 63.9
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type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 76.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 66.3
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value: 59.32626984126984
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value: 56.62535714285713
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value: 66.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.64999999999999
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value: 92.10000000000001
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type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.8076626877166
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value: 85.71428571428571
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.88888888888889
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.5
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value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 61.0062893081761
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value: 55.13976240391334
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value: 52.92112499659669
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value: 61.0062893081761
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.5
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value: 85.69166666666668
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value: 89.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 73.54085603112841
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 43.58974358974359
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value: 43.58974358974359
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 59.599999999999994
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value: 59.599999999999994
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.2
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value: 81.61666666666665
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value: 80.02833333333335
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value: 85.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 63.78504672897196
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type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 66.5
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value: 66.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.6
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value: 84.27916666666665
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value: 88.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 58.699999999999996
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value: 50.63214035964035
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value: 58.699999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.10000000000001
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value: 89.03333333333333
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value: 92.10000000000001
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type: BitextMining
dataset:
name: MTEB Tatoeba (ita-eng)
type: mteb/tatoeba-bitext-mining
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.10000000000001
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value: 86.36166666666668
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value: 90.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.4
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value: 88.89000000000001
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value: 87.71166666666666
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value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lvs-eng)
type: mteb/tatoeba-bitext-mining
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.7
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value: 60.67427750410509
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value: 58.71785714285714
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value: 65.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (glg-eng)
type: mteb/tatoeba-bitext-mining
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.39999999999999
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value: 81.93190476190475
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value: 80.37833333333333
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value: 85.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ceb-eng)
type: mteb/tatoeba-bitext-mining
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 47.833333333333336
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value: 42.006625781625786
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value: 40.077380952380956
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value: 47.833333333333336
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type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.4
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value: 8.24465007215007
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value: 7.664597069597071
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value: 10.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 82.6
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value: 77.76333333333334
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value: 75.57833333333332
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value: 82.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swg-eng)
type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 52.67857142857143
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value: 44.302721088435376
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value: 41.49801587301587
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value: 52.67857142857143
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arq-eng)
type: mteb/tatoeba-bitext-mining
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 28.3205268935236
- type: f1
value: 22.426666605171157
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value: 20.685900116470915
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value: 28.3205268935236
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kab-eng)
type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 22.7
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value: 17.833970473970474
- type: precision
value: 16.407335164835164
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value: 22.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fra-eng)
type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
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value: 89.92999999999999
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value: 88.87
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.25
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value: 88.21666666666667
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value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.19999999999999
- type: f1
value: 63.38269841269841
- type: precision
value: 61.14773809523809
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value: 69.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.8
- type: f1
value: 42.839915639915645
- type: precision
value: 40.770287114845935
- type: recall
value: 48.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.8
- type: f1
value: 85.90666666666668
- type: precision
value: 84.54166666666666
- type: recall
value: 88.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.6
- type: f1
value: 40.85892920804686
- type: precision
value: 38.838223114604695
- type: recall
value: 46.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.0
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value: 78.45333333333333
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value: 84.0
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type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.5
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value: 86.5
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value: 90.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (nno-eng)
type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 74.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 32.846715328467155
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type: BitextMining
dataset:
name: MTEB Tatoeba (mhr-eng)
type: mteb/tatoeba-bitext-mining
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.0
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value: 5.544177607179943
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value: 8.0
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type: BitextMining
dataset:
name: MTEB Tatoeba (dan-eng)
type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.6
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value: 87.6
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type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.5
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value: 82.47333333333333
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value: 87.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (amh-eng)
type: mteb/tatoeba-bitext-mining
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.95238095238095
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type: BitextMining
dataset:
name: MTEB Tatoeba (pam-eng)
type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.799999999999999
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 44.099378881987576
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type: BitextMining
dataset:
name: MTEB Tatoeba (srp-eng)
type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 84.3
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value: 84.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.5
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value: 92.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 10.0
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value: 7.878118605532398
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value: 10.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 79.22077922077922
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value: 79.22077922077922
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.64885496183206
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value: 55.992366412213734
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value: 65.64885496183206
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type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.06986899563319
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value: 96.06986899563319
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.2
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value: 71.72571428571428
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value: 69.41000000000001
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value: 77.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.4406779661017
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value: 81.74199623352166
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value: 86.4406779661017
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.4
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value: 6.017828743398003
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value: 5.4829865484756795
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value: 8.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 83.5
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value: 83.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 60.4
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value: 52.23242424242424
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value: 60.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 74.9
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value: 74.9
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type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.0
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value: 84.9652380952381
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value: 83.66166666666666
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value: 88.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.1
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value: 7.681244588744588
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value: 7.370043290043291
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value: 9.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.9651474530831
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value: 76.84220605132133
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value: 75.19606398962966
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value: 80.9651474530831
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
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value: 83.705
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value: 82.3120634920635
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value: 86.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 29.64426877470356
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value: 22.506399397703746
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value: 29.64426877470356
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 70.4225352112676
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value: 59.56572769953053
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value: 70.4225352112676
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 19.64071856287425
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.2
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value: 90.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 16.982385430661292
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value: 23.15270935960591
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 55.98591549295775
- task:
type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 64.06837606837607
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value: 73.07692307692307
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.89999999999999
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value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 37.78705636743215
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value: 29.72264397629742
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value: 37.78705636743215
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.6
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value: 16.91697302697303
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value: 15.71225147075147
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value: 21.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.01628664495115
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value: 79.83170466883823
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value: 85.01628664495115
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 83.39999999999999
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value: 79.96380952380952
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value: 78.48333333333333
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value: 83.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.2
- type: f1
value: 79.26190476190476
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value: 77.58833333333334
- type: recall
value: 83.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 75.59055118110236
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value: 71.66854143232096
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value: 70.30183727034121
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value: 75.59055118110236
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.5
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value: 59.26095238095238
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value: 56.81909090909092
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value: 65.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 55.26315789473685
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value: 47.986523325858506
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value: 45.33950006595436
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value: 55.26315789473685
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.89999999999999
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value: 78.835
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value: 77.04761904761905
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value: 82.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.269230769230774
- type: f1
value: 36.20421245421245
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value: 33.57371794871795
- type: recall
value: 43.269230769230774
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.70666666666666
- type: precision
value: 83.23166666666665
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.4
- type: f1
value: 72.54666666666667
- type: precision
value: 70.54318181818181
- type: recall
value: 77.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.60000000000001
- type: f1
value: 74.1588888888889
- type: precision
value: 72.30250000000001
- type: recall
value: 78.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.40566037735849
- type: f1
value: 66.82587328813744
- type: precision
value: 64.75039308176099
- type: recall
value: 72.40566037735849
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.8
- type: f1
value: 68.56357142857144
- type: precision
value: 66.3178822055138
- type: recall
value: 73.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.78832116788321
- type: f1
value: 89.3552311435523
- type: precision
value: 88.20559610705597
- type: recall
value: 91.78832116788321
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.05085581085581
- type: precision
value: 66.955
- type: recall
value: 74.3
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.896
- type: map_at_10
value: 8.993
- type: map_at_100
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value: 44.81
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value: 38.435
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value: 31.633
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value: 21.163
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value: 23.732
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value: 1.48
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value: 25.85
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value: 23.265
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value: 2.896
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value: 43.517
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value: 79.836
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value: 6.306000000000001
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value: 8.825
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
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value: 69.3874
- type: ap
value: 13.829909072469423
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value: 53.54534203543492
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.62026032823995
- type: f1
value: 62.85251350485221
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type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
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value: 33.21527881409797
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type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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- type: cos_sim_recall
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- type: dot_accuracy
value: 79.0546581629612
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value: 47.3197121792147
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value: 49.20106524633821
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value: 42.45499808502489
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- type: manhattan_accuracy
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- type: max_accuracy
value: 85.08076533349228
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value: 70.95016106374474
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value: 65.43987900176455
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type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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type: BitextMining
dataset:
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type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: 1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677
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dataset:
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type: facebook/belebele
config: rus_Cyrl-rus_Cyrl
split: test
revision: 75b399394a9803252cfec289d103de462763db7c
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dataset:
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type: facebook/belebele
config: rus_Cyrl-eng_Latn
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revision: 75b399394a9803252cfec289d103de462763db7c
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dataset:
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type: facebook/belebele
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type: BitextMining
dataset:
name: MTEB BibleNLPBitextMining (eng_Latn-rus_Cyrl)
type: davidstap/biblenlp-corpus-mmteb
config: eng_Latn-rus_Cyrl
split: train
revision: 264a18480c529d9e922483839b4b9758e690b762
metrics:
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value: 96.875
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dataset:
name: MTEB BibleNLPBitextMining (rus_Cyrl-eng_Latn)
type: davidstap/biblenlp-corpus-mmteb
config: rus_Cyrl-eng_Latn
split: train
revision: 264a18480c529d9e922483839b4b9758e690b762
metrics:
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value: 88.671875
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type: MultilabelClassification
dataset:
name: MTEB CEDRClassification (default)
type: ai-forever/cedr-classification
config: default
split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
metrics:
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- task:
type: Classification
dataset:
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type: tatiana-merz/cyrillic_turkic_langs
config: default
split: test
revision: e42d330f33d65b7b72dfd408883daf1661f06f18
metrics:
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dataset:
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type: mteb/flores
config: ace_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: bam_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: dzo_Tibt-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: hin_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: khm_Khmr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: mag_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: pap_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: sot_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: tur_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (ace_Latn-rus_Cyrl)
type: mteb/flores
config: ace_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (ban_Latn-rus_Cyrl)
type: mteb/flores
config: ban_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
config: ell_Grek-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hne_Deva-rus_Cyrl)
type: mteb/flores
config: hne_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 96.25352907961603
- type: main_score
value: 96.25352907961603
- type: precision
value: 96.02155091285526
- type: recall
value: 96.83794466403161
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kik_Latn-rus_Cyrl)
type: mteb/flores
config: kik_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 76.28458498023716
- type: f1
value: 73.5596919895859
- type: main_score
value: 73.5596919895859
- type: precision
value: 72.40900759055246
- type: recall
value: 76.28458498023716
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mai_Deva-rus_Cyrl)
type: mteb/flores
config: mai_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.72727272727273
- type: f1
value: 97.37812911725956
- type: main_score
value: 97.37812911725956
- type: precision
value: 97.26002258610953
- type: recall
value: 97.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pbt_Arab-rus_Cyrl)
type: mteb/flores
config: pbt_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.0711462450593
- type: f1
value: 93.34700387331966
- type: main_score
value: 93.34700387331966
- type: precision
value: 93.06920556920556
- type: recall
value: 94.0711462450593
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (spa_Latn-rus_Cyrl)
type: mteb/flores
config: spa_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
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value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (twi_Latn-rus_Cyrl)
type: mteb/flores
config: twi_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 77.77434363246721
- type: main_score
value: 77.77434363246721
- type: precision
value: 76.54444287596462
- type: recall
value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (acm_Arab-rus_Cyrl)
type: mteb/flores
config: acm_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.56521739130434
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value: 92.92490118577075
- type: main_score
value: 92.92490118577075
- type: precision
value: 92.16897233201581
- type: recall
value: 94.56521739130434
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bel_Cyrl-rus_Cyrl)
type: mteb/flores
config: bel_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.98550724637681
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value: 98.98550724637681
- type: precision
value: 98.88833992094862
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (eng_Latn-rus_Cyrl)
type: mteb/flores
config: eng_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.60474308300395
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value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hrv_Latn-rus_Cyrl)
type: mteb/flores
config: hrv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
- type: f1
value: 99.05138339920948
- type: main_score
value: 99.05138339920948
- type: precision
value: 99.00691699604744
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kin_Latn-rus_Cyrl)
type: mteb/flores
config: kin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 88.2411067193676
- type: f1
value: 86.5485246227658
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value: 86.5485246227658
- type: precision
value: 85.90652101521667
- type: recall
value: 88.2411067193676
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mal_Mlym-rus_Cyrl)
type: mteb/flores
config: mal_Mlym-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.51778656126481
- type: f1
value: 98.07971014492753
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value: 98.07971014492753
- type: precision
value: 97.88372859025033
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pes_Arab-rus_Cyrl)
type: mteb/flores
config: pes_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.51778656126481
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value: 98.0566534914361
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value: 98.0566534914361
- type: precision
value: 97.82608695652173
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (srd_Latn-rus_Cyrl)
type: mteb/flores
config: srd_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.6086956521739
- type: f1
value: 80.9173470979821
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value: 80.9173470979821
- type: precision
value: 80.24468672882627
- type: recall
value: 82.6086956521739
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tzm_Tfng-rus_Cyrl)
type: mteb/flores
config: tzm_Tfng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 7.41106719367589
- type: f1
value: 6.363562740945329
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value: 6.363562740945329
- type: precision
value: 6.090373175353411
- type: recall
value: 7.41106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (acq_Arab-rus_Cyrl)
type: mteb/flores
config: acq_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.25691699604744
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value: 93.81422924901187
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value: 93.81422924901187
- type: precision
value: 93.14064558629775
- type: recall
value: 95.25691699604744
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bem_Latn-rus_Cyrl)
type: mteb/flores
config: bem_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 68.08300395256917
- type: f1
value: 65.01368772860867
- type: main_score
value: 65.01368772860867
- type: precision
value: 63.91052337510628
- type: recall
value: 68.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (epo_Latn-rus_Cyrl)
type: mteb/flores
config: epo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.17193675889328
- type: main_score
value: 98.17193675889328
- type: precision
value: 98.08210564139418
- type: recall
value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hun_Latn-rus_Cyrl)
type: mteb/flores
config: hun_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
- type: f1
value: 99.1106719367589
- type: main_score
value: 99.1106719367589
- type: precision
value: 99.01185770750988
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kir_Cyrl-rus_Cyrl)
type: mteb/flores
config: kir_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.07549806364035
- type: main_score
value: 97.07549806364035
- type: precision
value: 96.90958498023716
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mar_Deva-rus_Cyrl)
type: mteb/flores
config: mar_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.44400527009222
- type: main_score
value: 97.44400527009222
- type: precision
value: 97.28966685488425
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (plt_Latn-rus_Cyrl)
type: mteb/flores
config: plt_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 79.9407114624506
- type: f1
value: 78.3154177760691
- type: main_score
value: 78.3154177760691
- type: precision
value: 77.69877344877344
- type: recall
value: 79.9407114624506
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (srp_Cyrl-rus_Cyrl)
type: mteb/flores
config: srp_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.70355731225297
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value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (uig_Arab-rus_Cyrl)
type: mteb/flores
config: uig_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.20158102766798
- type: f1
value: 81.44381923034585
- type: main_score
value: 81.44381923034585
- type: precision
value: 80.78813411582477
- type: recall
value: 83.20158102766798
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (aeb_Arab-rus_Cyrl)
type: mteb/flores
config: aeb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.20553359683794
- type: f1
value: 88.75352907961603
- type: main_score
value: 88.75352907961603
- type: precision
value: 87.64328063241106
- type: recall
value: 91.20553359683794
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ben_Beng-rus_Cyrl)
type: mteb/flores
config: ben_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.60671936758894
- type: main_score
value: 98.60671936758894
- type: precision
value: 98.4766139657444
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (est_Latn-rus_Cyrl)
type: mteb/flores
config: est_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.24505928853755
- type: f1
value: 95.27417027417027
- type: main_score
value: 95.27417027417027
- type: precision
value: 94.84107378129117
- type: recall
value: 96.24505928853755
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hye_Armn-rus_Cyrl)
type: mteb/flores
config: hye_Armn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.67786561264822
- type: main_score
value: 97.67786561264822
- type: precision
value: 97.55839022637441
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kmb_Latn-rus_Cyrl)
type: mteb/flores
config: kmb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 46.047430830039524
- type: f1
value: 42.94464804804471
- type: main_score
value: 42.94464804804471
- type: precision
value: 41.9851895607238
- type: recall
value: 46.047430830039524
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (min_Arab-rus_Cyrl)
type: mteb/flores
config: min_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 3.9525691699604746
- type: f1
value: 3.402665192725756
- type: main_score
value: 3.402665192725756
- type: precision
value: 3.303787557740127
- type: recall
value: 3.9525691699604746
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pol_Latn-rus_Cyrl)
type: mteb/flores
config: pol_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ssw_Latn-rus_Cyrl)
type: mteb/flores
config: ssw_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 73.22134387351778
- type: f1
value: 70.43086049508975
- type: main_score
value: 70.43086049508975
- type: precision
value: 69.35312022355656
- type: recall
value: 73.22134387351778
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ukr_Cyrl-rus_Cyrl)
type: mteb/flores
config: ukr_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.90118577075098
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value: 99.86824769433464
- type: main_score
value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (afr_Latn-rus_Cyrl)
type: mteb/flores
config: afr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bho_Deva-rus_Cyrl)
type: mteb/flores
config: bho_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.12182382834557
- type: main_score
value: 93.12182382834557
- type: precision
value: 92.7523453232338
- type: recall
value: 94.0711462450593
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (eus_Latn-rus_Cyrl)
type: mteb/flores
config: eus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 92.19367588932806
- type: f1
value: 91.23604975587072
- type: main_score
value: 91.23604975587072
- type: precision
value: 90.86697443588663
- type: recall
value: 92.19367588932806
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ibo_Latn-rus_Cyrl)
type: mteb/flores
config: ibo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 82.21343873517787
- type: f1
value: 80.17901604858126
- type: main_score
value: 80.17901604858126
- type: precision
value: 79.3792284780028
- type: recall
value: 82.21343873517787
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kmr_Latn-rus_Cyrl)
type: mteb/flores
config: kmr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.67588932806325
- type: f1
value: 66.72311714750278
- type: main_score
value: 66.72311714750278
- type: precision
value: 66.00178401554004
- type: recall
value: 68.67588932806325
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (min_Latn-rus_Cyrl)
type: mteb/flores
config: min_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 78.65612648221344
- type: f1
value: 76.26592719972166
- type: main_score
value: 76.26592719972166
- type: precision
value: 75.39980459997484
- type: recall
value: 78.65612648221344
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (por_Latn-rus_Cyrl)
type: mteb/flores
config: por_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 95.9669678147939
- type: main_score
value: 95.9669678147939
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value: 95.59453227931488
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value: 96.83794466403161
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (sun_Latn-rus_Cyrl)
type: mteb/flores
config: sun_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (umb_Latn-rus_Cyrl)
type: mteb/flores
config: umb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 41.00790513833992
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ajp_Arab-rus_Cyrl)
type: mteb/flores
config: ajp_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bjn_Arab-rus_Cyrl)
type: mteb/flores
config: bjn_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ewe_Latn-rus_Cyrl)
type: mteb/flores
config: ewe_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ilo_Latn-rus_Cyrl)
type: mteb/flores
config: ilo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.10276679841897
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (knc_Arab-rus_Cyrl)
type: mteb/flores
config: knc_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 10.079051383399209
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (mkd_Cyrl-rus_Cyrl)
type: mteb/flores
config: mkd_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (prs_Arab-rus_Cyrl)
type: mteb/flores
config: prs_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (swe_Latn-rus_Cyrl)
type: mteb/flores
config: swe_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (urd_Arab-rus_Cyrl)
type: mteb/flores
config: urd_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (aka_Latn-rus_Cyrl)
type: mteb/flores
config: aka_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.22529644268775
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bjn_Latn-rus_Cyrl)
type: mteb/flores
config: bjn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (fao_Latn-rus_Cyrl)
type: mteb/flores
config: fao_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.9288537549407
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ind_Latn-rus_Cyrl)
type: mteb/flores
config: ind_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (knc_Latn-rus_Cyrl)
type: mteb/flores
config: knc_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 33.49802371541502
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (mlt_Latn-rus_Cyrl)
type: mteb/flores
config: mlt_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.40316205533597
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (quy_Latn-rus_Cyrl)
type: mteb/flores
config: quy_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 40.612648221343875
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (swh_Latn-rus_Cyrl)
type: mteb/flores
config: swh_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (uzn_Latn-rus_Cyrl)
type: mteb/flores
config: uzn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.83794466403161
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (als_Latn-rus_Cyrl)
type: mteb/flores
config: als_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.6142480707698
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value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bod_Tibt-rus_Cyrl)
type: mteb/flores
config: bod_Tibt-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 1.0869565217391304
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fij_Latn-rus_Cyrl)
type: mteb/flores
config: fij_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 59.32326368115546
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value: 63.24110671936759
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (isl_Latn-rus_Cyrl)
type: mteb/flores
config: isl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.03162055335969
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value: 86.65991814698712
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value: 89.03162055335969
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kon_Latn-rus_Cyrl)
type: mteb/flores
config: kon_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.91304347826086
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value: 70.58714102449801
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value: 73.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mni_Beng-rus_Cyrl)
type: mteb/flores
config: mni_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 29.545454545454547
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value: 26.983849851025344
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value: 29.545454545454547
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ron_Latn-rus_Cyrl)
type: mteb/flores
config: ron_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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value: 99.2094861660079
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value: 99.1106719367589
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value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (szl_Latn-rus_Cyrl)
type: mteb/flores
config: szl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.26482213438736
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value: 85.18912031587512
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value: 84.77199409959775
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value: 86.26482213438736
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (vec_Latn-rus_Cyrl)
type: mteb/flores
config: vec_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.67193675889328
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value: 84.62529734716581
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value: 84.62529734716581
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value: 84.2611422440705
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value: 85.67193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (amh_Ethi-rus_Cyrl)
type: mteb/flores
config: amh_Ethi-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.76284584980237
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value: 93.91735076517685
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value: 93.91735076517685
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value: 93.57553798858147
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value: 94.76284584980237
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bos_Latn-rus_Cyrl)
type: mteb/flores
config: bos_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 99.05655938264634
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value: 99.05655938264634
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value: 99.01185770750988
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value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fin_Latn-rus_Cyrl)
type: mteb/flores
config: fin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.43741765480895
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value: 97.43741765480895
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value: 97.1590909090909
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ita_Latn-rus_Cyrl)
type: mteb/flores
config: ita_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.70355731225297
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value: 99.60474308300395
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value: 99.60474308300395
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value: 99.55533596837944
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value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kor_Hang-rus_Cyrl)
type: mteb/flores
config: kor_Hang-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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value: 96.49868247694334
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value: 96.49868247694334
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value: 96.10507246376811
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mos_Latn-rus_Cyrl)
type: mteb/flores
config: mos_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 34.683794466403164
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value: 32.766819308009076
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value: 32.766819308009076
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value: 32.1637493670237
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value: 34.683794466403164
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (run_Latn-rus_Cyrl)
type: mteb/flores
config: run_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.399209486166
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value: 81.10578750604326
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value: 81.10578750604326
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value: 80.16763162673529
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value: 83.399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tam_Taml-rus_Cyrl)
type: mteb/flores
config: tam_Taml-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.41897233201581
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value: 98.01548089591567
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value: 98.01548089591567
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value: 97.84020327498588
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value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (vie_Latn-rus_Cyrl)
type: mteb/flores
config: vie_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
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value: 98.81422924901186
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value: 98.81422924901186
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value: 98.66600790513834
- type: recall
value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (apc_Arab-rus_Cyrl)
type: mteb/flores
config: apc_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.87351778656127
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value: 92.10803689064558
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value: 92.10803689064558
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value: 91.30434782608695
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value: 93.87351778656127
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bug_Latn-rus_Cyrl)
type: mteb/flores
config: bug_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 57.608695652173914
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value: 54.95878654927162
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value: 54.95878654927162
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value: 54.067987427805654
- type: recall
value: 57.608695652173914
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fon_Latn-rus_Cyrl)
type: mteb/flores
config: fon_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 58.06537275812945
- type: precision
value: 56.554057596959204
- type: recall
value: 61.95652173913043
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (jav_Latn-rus_Cyrl)
type: mteb/flores
config: jav_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.09168143201127
- type: recall
value: 93.47826086956522
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lao_Laoo-rus_Cyrl)
type: mteb/flores
config: lao_Laoo-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.76104922745239
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value: 89.24754593232855
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value: 91.10671936758892
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mri_Latn-rus_Cyrl)
type: mteb/flores
config: mri_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 71.14624505928853
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value: 67.15942311051006
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value: 71.14624505928853
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ace_Arab)
type: mteb/flores
config: rus_Cyrl-ace_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 15.478527409347508
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value: 19.565217391304348
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bam_Latn)
type: mteb/flores
config: rus_Cyrl-bam_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 66.96012924273795
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value: 73.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dzo_Tibt)
type: mteb/flores
config: rus_Cyrl-dzo_Tibt
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 0.592885375494071
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hin_Deva)
type: mteb/flores
config: rus_Cyrl-hin_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.85177865612648
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value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-khm_Khmr)
type: mteb/flores
config: rus_Cyrl-khm_Khmr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.81686429512516
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value: 97.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mag_Deva)
type: mteb/flores
config: rus_Cyrl-mag_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.29183135704875
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value: 99.50592885375494
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pap_Latn)
type: mteb/flores
config: rus_Cyrl-pap_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.93675889328063
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sot_Latn)
type: mteb/flores
config: rus_Cyrl-sot_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.67588932806325
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tur_Latn)
type: mteb/flores
config: rus_Cyrl-tur_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ace_Latn)
type: mteb/flores
config: rus_Cyrl-ace_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 74.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ban_Latn)
type: mteb/flores
config: rus_Cyrl-ban_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 77.76208475761422
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value: 81.7193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ell_Grek)
type: mteb/flores
config: rus_Cyrl-ell_Grek
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hne_Deva)
type: mteb/flores
config: rus_Cyrl-hne_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kik_Latn)
type: mteb/flores
config: rus_Cyrl-kik_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 74.63877909530083
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value: 80.73122529644269
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mai_Deva)
type: mteb/flores
config: rus_Cyrl-mai_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pbt_Arab)
type: mteb/flores
config: rus_Cyrl-pbt_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-spa_Latn)
type: mteb/flores
config: rus_Cyrl-spa_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
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value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-twi_Latn)
type: mteb/flores
config: rus_Cyrl-twi_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.01581027667984
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value: 76.43272186750448
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value: 82.01581027667984
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-acm_Arab)
type: mteb/flores
config: rus_Cyrl-acm_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.3201581027668
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value: 97.76021080368908
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value: 97.48023715415019
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value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bel_Cyrl)
type: mteb/flores
config: rus_Cyrl-bel_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.22134387351778
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value: 97.4308300395257
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value: 98.22134387351778
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-eng_Latn)
type: mteb/flores
config: rus_Cyrl-eng_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.70355731225297
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value: 99.55533596837944
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value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hrv_Latn)
type: mteb/flores
config: rus_Cyrl-hrv_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.69894598155466
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value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kin_Latn)
type: mteb/flores
config: rus_Cyrl-kin_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.37944664031622
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value: 90.71475625823452
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value: 93.37944664031622
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mal_Mlym)
type: mteb/flores
config: rus_Cyrl-mal_Mlym
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
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value: 99.07773386034255
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value: 98.96245059288538
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value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pes_Arab)
type: mteb/flores
config: rus_Cyrl-pes_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.30368906455863
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value: 98.10606060606061
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-srd_Latn)
type: mteb/flores
config: rus_Cyrl-srd_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.03162055335969
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value: 86.11048371917937
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value: 86.11048371917937
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value: 84.86001317523056
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value: 89.03162055335969
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tzm_Tfng)
type: mteb/flores
config: rus_Cyrl-tzm_Tfng
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 12.351778656126482
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value: 10.112177999067715
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value: 10.112177999067715
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value: 9.53495885438645
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value: 12.351778656126482
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-acq_Arab)
type: mteb/flores
config: rus_Cyrl-acq_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
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value: 98.55072463768116
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value: 98.36956521739131
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value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bem_Latn)
type: mteb/flores
config: rus_Cyrl-bem_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.22134387351778
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value: 66.40073447632736
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value: 73.22134387351778
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-epo_Latn)
type: mteb/flores
config: rus_Cyrl-epo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.66600790513834
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value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hun_Latn)
type: mteb/flores
config: rus_Cyrl-hun_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.88274044795784
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value: 95.45454545454545
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value: 96.83794466403161
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kir_Cyrl)
type: mteb/flores
config: rus_Cyrl-kir_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.34387351778656
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value: 95.14163372859026
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value: 96.34387351778656
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mar_Deva)
type: mteb/flores
config: rus_Cyrl-mar_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.07312252964427
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value: 98.71541501976284
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-plt_Latn)
type: mteb/flores
config: rus_Cyrl-plt_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 88.04347826086956
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value: 85.14328063241106
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value: 85.14328063241106
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value: 83.96339168078298
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value: 88.04347826086956
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-srp_Cyrl)
type: mteb/flores
config: rus_Cyrl-srp_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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value: 99.2094861660079
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value: 99.1106719367589
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value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-uig_Arab)
type: mteb/flores
config: rus_Cyrl-uig_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.19367588932806
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value: 89.98541313758706
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value: 89.01021080368906
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value: 92.19367588932806
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-aeb_Arab)
type: mteb/flores
config: rus_Cyrl-aeb_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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split: devtest
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dataset:
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dataset:
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dataset:
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split: devtest
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metrics:
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dataset:
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dataset:
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type: mteb/flores
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split: devtest
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type: BitextMining
dataset:
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
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dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
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split: devtest
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mya_Mymr)
type: mteb/flores
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split: devtest
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-taq_Tfng)
type: mteb/flores
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split: devtest
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metrics:
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dataset:
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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split: devtest
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metrics:
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
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metrics:
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type: BitextMining
dataset:
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split: devtest
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metrics:
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type: BitextMining
dataset:
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.81818181818183
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-san_Deva)
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.47826086956522
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tat_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-xho_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.08300395256917
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ars_Arab)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ceb_Latn)
type: mteb/flores
config: rus_Cyrl-ceb_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.65217391304348
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value: 94.3544137022398
- type: main_score
value: 94.3544137022398
- type: precision
value: 93.76646903820817
- type: recall
value: 95.65217391304348
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-fuv_Latn)
type: mteb/flores
config: rus_Cyrl-fuv_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 51.18577075098815
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value: 44.5990252610806
- type: main_score
value: 44.5990252610806
- type: precision
value: 42.34331599450177
- type: recall
value: 51.18577075098815
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kac_Latn)
type: mteb/flores
config: rus_Cyrl-kac_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 46.93675889328063
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value: 41.79004018701787
- type: main_score
value: 41.79004018701787
- type: precision
value: 40.243355662392624
- type: recall
value: 46.93675889328063
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lin_Latn)
type: mteb/flores
config: rus_Cyrl-lin_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.50197628458498
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value: 89.1205533596838
- type: main_score
value: 89.1205533596838
- type: precision
value: 88.07147562582345
- type: recall
value: 91.50197628458498
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nno_Latn)
type: mteb/flores
config: rus_Cyrl-nno_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.81422924901186
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value: 98.41897233201581
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value: 98.41897233201581
- type: precision
value: 98.22134387351778
- type: recall
value: 98.81422924901186
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sat_Olck)
type: mteb/flores
config: rus_Cyrl-sat_Olck
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 2.371541501976284
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value: 1.0726274943087382
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value: 1.0726274943087382
- type: precision
value: 0.875279634748803
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value: 2.371541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tel_Telu)
type: mteb/flores
config: rus_Cyrl-tel_Telu
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.68247694334651
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value: 98.68247694334651
- type: precision
value: 98.51778656126481
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ydd_Hebr)
type: mteb/flores
config: rus_Cyrl-ydd_Hebr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.42687747035573
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value: 86.47609636740073
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value: 86.47609636740073
- type: precision
value: 85.13669301712781
- type: recall
value: 89.42687747035573
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ary_Arab)
type: mteb/flores
config: rus_Cyrl-ary_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.82213438735178
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value: 87.04545454545456
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value: 87.04545454545456
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value: 85.76910408432148
- type: recall
value: 89.82213438735178
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ces_Latn)
type: mteb/flores
config: rus_Cyrl-ces_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
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value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gaz_Latn)
type: mteb/flores
config: rus_Cyrl-gaz_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 64.9209486166008
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value: 58.697458119394874
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value: 58.697458119394874
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value: 56.43402189597842
- type: recall
value: 64.9209486166008
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kam_Latn)
type: mteb/flores
config: rus_Cyrl-kam_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 59.18972332015811
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value: 53.19031511966295
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value: 53.19031511966295
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value: 51.08128357343655
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value: 59.18972332015811
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lit_Latn)
type: mteb/flores
config: rus_Cyrl-lit_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.54150197628458
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value: 95.5368906455863
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value: 95.5368906455863
- type: precision
value: 95.0592885375494
- type: recall
value: 96.54150197628458
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nob_Latn)
type: mteb/flores
config: rus_Cyrl-nob_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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value: 97.51317523056655
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value: 97.51317523056655
- type: precision
value: 97.2167325428195
- type: recall
value: 98.12252964426878
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-scn_Latn)
type: mteb/flores
config: rus_Cyrl-scn_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.0909090909091
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value: 80.37000439174352
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value: 80.37000439174352
- type: precision
value: 78.83994628559846
- type: recall
value: 84.0909090909091
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tgk_Cyrl)
type: mteb/flores
config: rus_Cyrl-tgk_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.68774703557312
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value: 90.86344814605684
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value: 90.86344814605684
- type: precision
value: 90.12516469038208
- type: recall
value: 92.68774703557312
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-yor_Latn)
type: mteb/flores
config: rus_Cyrl-yor_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 72.13438735177866
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value: 66.78759646150951
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value: 66.78759646150951
- type: precision
value: 64.85080192096002
- type: recall
value: 72.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-arz_Arab)
type: mteb/flores
config: rus_Cyrl-arz_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.364953886693
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value: 97.364953886693
- type: precision
value: 97.03557312252964
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-cjk_Latn)
type: mteb/flores
config: rus_Cyrl-cjk_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 51.976284584980235
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value: 46.468762353149714
- type: main_score
value: 46.468762353149714
- type: precision
value: 44.64073366247278
- type: recall
value: 51.976284584980235
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gla_Latn)
type: mteb/flores
config: rus_Cyrl-gla_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 79.74308300395256
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value: 75.55611165294958
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value: 75.55611165294958
- type: precision
value: 73.95033408620365
- type: recall
value: 79.74308300395256
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kan_Knda)
type: mteb/flores
config: rus_Cyrl-kan_Knda
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
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value: 98.96245059288538
- type: main_score
value: 98.96245059288538
- type: precision
value: 98.84716732542819
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lmo_Latn)
type: mteb/flores
config: rus_Cyrl-lmo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.41106719367589
- type: f1
value: 78.56413514022209
- type: main_score
value: 78.56413514022209
- type: precision
value: 77.15313068573938
- type: recall
value: 82.41106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-npi_Deva)
type: mteb/flores
config: rus_Cyrl-npi_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.3201581027668
- type: main_score
value: 98.3201581027668
- type: precision
value: 98.12252964426878
- type: recall
value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-shn_Mymr)
type: mteb/flores
config: rus_Cyrl-shn_Mymr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 57.11462450592886
- type: f1
value: 51.51361369197337
- type: main_score
value: 51.51361369197337
- type: precision
value: 49.71860043649573
- type: recall
value: 57.11462450592886
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tgl_Latn)
type: mteb/flores
config: rus_Cyrl-tgl_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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value: 97.18379446640316
- type: main_score
value: 97.18379446640316
- type: precision
value: 96.88735177865613
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-yue_Hant)
type: mteb/flores
config: rus_Cyrl-yue_Hant
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
- type: f1
value: 99.09420289855072
- type: main_score
value: 99.09420289855072
- type: precision
value: 98.9953886693017
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-asm_Beng)
type: mteb/flores
config: rus_Cyrl-asm_Beng
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.55335968379447
- type: f1
value: 94.16007905138339
- type: main_score
value: 94.16007905138339
- type: precision
value: 93.50296442687747
- type: recall
value: 95.55335968379447
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ckb_Arab)
type: mteb/flores
config: rus_Cyrl-ckb_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 92.88537549407114
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value: 90.76745718050066
- type: main_score
value: 90.76745718050066
- type: precision
value: 89.80072463768116
- type: recall
value: 92.88537549407114
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gle_Latn)
type: mteb/flores
config: rus_Cyrl-gle_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.699604743083
- type: f1
value: 89.40899680030115
- type: main_score
value: 89.40899680030115
- type: precision
value: 88.40085638998683
- type: recall
value: 91.699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kas_Arab)
type: mteb/flores
config: rus_Cyrl-kas_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 88.3399209486166
- type: f1
value: 85.14351590438548
- type: main_score
value: 85.14351590438548
- type: precision
value: 83.72364953886692
- type: recall
value: 88.3399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ltg_Latn)
type: mteb/flores
config: rus_Cyrl-ltg_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 83.399209486166
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value: 79.88408934061107
- type: main_score
value: 79.88408934061107
- type: precision
value: 78.53794509179885
- type: recall
value: 83.399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nso_Latn)
type: mteb/flores
config: rus_Cyrl-nso_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.20553359683794
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value: 88.95406635525212
- type: main_score
value: 88.95406635525212
- type: precision
value: 88.01548089591567
- type: recall
value: 91.20553359683794
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sin_Sinh)
type: mteb/flores
config: rus_Cyrl-sin_Sinh
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.91304347826086
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value: 98.56719367588933
- type: main_score
value: 98.56719367588933
- type: precision
value: 98.40250329380763
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tha_Thai)
type: mteb/flores
config: rus_Cyrl-tha_Thai
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.94861660079052
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value: 94.66403162055336
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value: 94.66403162055336
- type: precision
value: 94.03820816864295
- type: recall
value: 95.94861660079052
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hans)
type: mteb/flores
config: rus_Cyrl-zho_Hans
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.4308300395257
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value: 96.5909090909091
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value: 96.5909090909091
- type: precision
value: 96.17918313570487
- type: recall
value: 97.4308300395257
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ast_Latn)
type: mteb/flores
config: rus_Cyrl-ast_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.46640316205533
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value: 92.86890645586297
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value: 92.86890645586297
- type: precision
value: 92.14756258234519
- type: recall
value: 94.46640316205533
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-crh_Latn)
type: mteb/flores
config: rus_Cyrl-crh_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.66403162055336
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value: 93.2663592446201
- type: main_score
value: 93.2663592446201
- type: precision
value: 92.66716073781292
- type: recall
value: 94.66403162055336
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-glg_Latn)
type: mteb/flores
config: rus_Cyrl-glg_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.81422924901186
- type: f1
value: 98.46837944664031
- type: main_score
value: 98.46837944664031
- type: precision
value: 98.3201581027668
- type: recall
value: 98.81422924901186
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kas_Deva)
type: mteb/flores
config: rus_Cyrl-kas_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 69.1699604743083
- type: f1
value: 63.05505292906477
- type: main_score
value: 63.05505292906477
- type: precision
value: 60.62594108789761
- type: recall
value: 69.1699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ltz_Latn)
type: mteb/flores
config: rus_Cyrl-ltz_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.40316205533597
- type: f1
value: 89.26571616789009
- type: main_score
value: 89.26571616789009
- type: precision
value: 88.40179747788443
- type: recall
value: 91.40316205533597
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nus_Latn)
type: mteb/flores
config: rus_Cyrl-nus_Latn
split: devtest
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value: 32.56257884802308
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value: 38.93280632411067
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-slk_Latn)
type: mteb/flores
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tir_Ethi)
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type: BitextMining
dataset:
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type: BitextMining
dataset:
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dataset:
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dataset:
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metrics:
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type: BitextMining
dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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split: devtest
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metrics:
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dataset:
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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metrics:
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dataset:
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revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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dataset:
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type: mteb/flores
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split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ory_Orya)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sna_Latn)
type: mteb/flores
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split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tso_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-azj_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dik_Latn)
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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split: devtest
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metrics:
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type: BitextMining
dataset:
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split: devtest
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metrics:
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dataset:
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metrics:
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type: BitextMining
dataset:
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
config: rus_Cyrl-snd_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.8498023715415
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tuk_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
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value: 61.191590257043934
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value: 68.08300395256917
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bak_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.04743083003953
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dyu_Latn)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 37.45059288537549
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-heb_Hebr)
type: mteb/flores
config: rus_Cyrl-heb_Hebr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.23320158102767
- type: f1
value: 96.38998682476942
- type: main_score
value: 96.38998682476942
- type: precision
value: 95.99802371541502
- type: recall
value: 97.23320158102767
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-khk_Cyrl)
type: mteb/flores
config: rus_Cyrl-khk_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.00724637681158
- type: main_score
value: 98.00724637681158
- type: precision
value: 97.82938076416336
- type: recall
value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lvs_Latn)
type: mteb/flores
config: rus_Cyrl-lvs_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.4308300395257
- type: f1
value: 96.61396574440053
- type: main_score
value: 96.61396574440053
- type: precision
value: 96.2203557312253
- type: recall
value: 97.4308300395257
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pan_Guru)
type: mteb/flores
config: rus_Cyrl-pan_Guru
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034256
- type: main_score
value: 99.07773386034256
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-som_Latn)
type: mteb/flores
config: rus_Cyrl-som_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 87.74703557312253
- type: f1
value: 84.52898550724638
- type: main_score
value: 84.52898550724638
- type: precision
value: 83.09288537549409
- type: recall
value: 87.74703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tum_Latn)
type: mteb/flores
config: rus_Cyrl-tum_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 87.15415019762845
- type: f1
value: 83.85069640504425
- type: main_score
value: 83.85069640504425
- type: precision
value: 82.43671183888576
- type: recall
value: 87.15415019762845
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (taq_Latn-rus_Cyrl)
type: mteb/flores
config: taq_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 28.55731225296443
- type: f1
value: 26.810726360049568
- type: main_score
value: 26.810726360049568
- type: precision
value: 26.260342858265577
- type: recall
value: 28.55731225296443
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (war_Latn-rus_Cyrl)
type: mteb/flores
config: war_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.86166007905138
- type: f1
value: 94.03147083483051
- type: main_score
value: 94.03147083483051
- type: precision
value: 93.70653606003322
- type: recall
value: 94.86166007905138
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arb_Arab-rus_Cyrl)
type: mteb/flores
config: arb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.34387351778656
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value: 95.23056653491436
- type: main_score
value: 95.23056653491436
- type: precision
value: 94.70520421607378
- type: recall
value: 96.34387351778656
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bul_Cyrl-rus_Cyrl)
type: mteb/flores
config: bul_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.90118577075098
- type: f1
value: 99.86824769433464
- type: main_score
value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fra_Latn-rus_Cyrl)
type: mteb/flores
config: fra_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (jpn_Jpan-rus_Cyrl)
type: mteb/flores
config: jpn_Jpan-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.3201581027668
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value: 97.76021080368905
- type: main_score
value: 97.76021080368905
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lij_Latn-rus_Cyrl)
type: mteb/flores
config: lij_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 83.49802371541502
- type: f1
value: 81.64800059239636
- type: main_score
value: 81.64800059239636
- type: precision
value: 80.9443055878478
- type: recall
value: 83.49802371541502
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mya_Mymr-rus_Cyrl)
type: mteb/flores
config: mya_Mymr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 88.76776366313682
- type: main_score
value: 88.76776366313682
- type: precision
value: 88.18370446119435
- type: recall
value: 90.21739130434783
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sag_Latn-rus_Cyrl)
type: mteb/flores
config: sag_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 41.699604743083
- type: f1
value: 39.53066322643847
- type: main_score
value: 39.53066322643847
- type: precision
value: 38.822876239229274
- type: recall
value: 41.699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (taq_Tfng-rus_Cyrl)
type: mteb/flores
config: taq_Tfng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 10.67193675889328
- type: f1
value: 9.205744965817951
- type: main_score
value: 9.205744965817951
- type: precision
value: 8.85195219073817
- type: recall
value: 10.67193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (wol_Latn-rus_Cyrl)
type: mteb/flores
config: wol_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 63.537549407114625
- type: f1
value: 60.65190727391827
- type: main_score
value: 60.65190727391827
- type: precision
value: 59.61144833427442
- type: recall
value: 63.537549407114625
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arb_Latn-rus_Cyrl)
type: mteb/flores
config: arb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 13.142292490118576
- type: f1
value: 12.372910318176764
- type: main_score
value: 12.372910318176764
- type: precision
value: 12.197580895919188
- type: recall
value: 13.142292490118576
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cat_Latn-rus_Cyrl)
type: mteb/flores
config: cat_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.01185770750988
- type: f1
value: 98.80599472990777
- type: main_score
value: 98.80599472990777
- type: precision
value: 98.72953133822698
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fur_Latn-rus_Cyrl)
type: mteb/flores
config: fur_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 81.02766798418972
- type: f1
value: 79.36184294084613
- type: main_score
value: 79.36184294084613
- type: precision
value: 78.69187826527705
- type: recall
value: 81.02766798418972
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kab_Latn-rus_Cyrl)
type: mteb/flores
config: kab_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 34.387351778656125
- type: f1
value: 32.02306921576947
- type: main_score
value: 32.02306921576947
- type: precision
value: 31.246670347137467
- type: recall
value: 34.387351778656125
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lim_Latn-rus_Cyrl)
type: mteb/flores
config: lim_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 78.26086956521739
- type: f1
value: 75.90239449214359
- type: main_score
value: 75.90239449214359
- type: precision
value: 75.02211430745493
- type: recall
value: 78.26086956521739
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nld_Latn-rus_Cyrl)
type: mteb/flores
config: nld_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (san_Deva-rus_Cyrl)
type: mteb/flores
config: san_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 87.94466403162056
- type: f1
value: 86.68928897189767
- type: main_score
value: 86.68928897189767
- type: precision
value: 86.23822997079216
- type: recall
value: 87.94466403162056
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tat_Cyrl-rus_Cyrl)
type: mteb/flores
config: tat_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.03557312252964
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value: 96.4167365353136
- type: main_score
value: 96.4167365353136
- type: precision
value: 96.16847826086958
- type: recall
value: 97.03557312252964
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (xho_Latn-rus_Cyrl)
type: mteb/flores
config: xho_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.95652173913044
- type: f1
value: 85.5506497283435
- type: main_score
value: 85.5506497283435
- type: precision
value: 84.95270479733395
- type: recall
value: 86.95652173913044
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ars_Arab-rus_Cyrl)
type: mteb/flores
config: ars_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.6403162055336
- type: f1
value: 95.60935441370223
- type: main_score
value: 95.60935441370223
- type: precision
value: 95.13339920948617
- type: recall
value: 96.6403162055336
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ceb_Latn-rus_Cyrl)
type: mteb/flores
config: ceb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.7509881422925
- type: f1
value: 95.05209198303827
- type: main_score
value: 95.05209198303827
- type: precision
value: 94.77662283368805
- type: recall
value: 95.7509881422925
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fuv_Latn-rus_Cyrl)
type: mteb/flores
config: fuv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 45.25691699604743
- type: f1
value: 42.285666666742365
- type: main_score
value: 42.285666666742365
- type: precision
value: 41.21979853402283
- type: recall
value: 45.25691699604743
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kac_Latn-rus_Cyrl)
type: mteb/flores
config: kac_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 34.683794466403164
- type: f1
value: 33.3235346229031
- type: main_score
value: 33.3235346229031
- type: precision
value: 32.94673924616852
- type: recall
value: 34.683794466403164
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lin_Latn-rus_Cyrl)
type: mteb/flores
config: lin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.85770750988142
- type: f1
value: 85.1867110799439
- type: main_score
value: 85.1867110799439
- type: precision
value: 84.53038212173273
- type: recall
value: 86.85770750988142
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nno_Latn-rus_Cyrl)
type: mteb/flores
config: nno_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.4308300395257
- type: f1
value: 96.78383210991906
- type: main_score
value: 96.78383210991906
- type: precision
value: 96.51185770750989
- type: recall
value: 97.4308300395257
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sat_Olck-rus_Cyrl)
type: mteb/flores
config: sat_Olck-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 1.185770750988142
- type: f1
value: 1.0279253129117258
- type: main_score
value: 1.0279253129117258
- type: precision
value: 1.0129746819135175
- type: recall
value: 1.185770750988142
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tel_Telu-rus_Cyrl)
type: mteb/flores
config: tel_Telu-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
- type: f1
value: 97.61198945981555
- type: main_score
value: 97.61198945981555
- type: precision
value: 97.401185770751
- type: recall
value: 98.12252964426878
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ydd_Hebr-rus_Cyrl)
type: mteb/flores
config: ydd_Hebr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 75.8893280632411
- type: f1
value: 74.00244008018511
- type: main_score
value: 74.00244008018511
- type: precision
value: 73.25683020960382
- type: recall
value: 75.8893280632411
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ary_Arab-rus_Cyrl)
type: mteb/flores
config: ary_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.56126482213439
- type: f1
value: 83.72796285839765
- type: main_score
value: 83.72796285839765
- type: precision
value: 82.65014273166447
- type: recall
value: 86.56126482213439
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ces_Latn-rus_Cyrl)
type: mteb/flores
config: ces_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (gaz_Latn-rus_Cyrl)
type: mteb/flores
config: gaz_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 42.58893280632411
- type: f1
value: 40.75832866805978
- type: main_score
value: 40.75832866805978
- type: precision
value: 40.14285046917723
- type: recall
value: 42.58893280632411
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kam_Latn-rus_Cyrl)
type: mteb/flores
config: kam_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 45.25691699604743
- type: f1
value: 42.6975518029456
- type: main_score
value: 42.6975518029456
- type: precision
value: 41.87472710984596
- type: recall
value: 45.25691699604743
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lit_Latn-rus_Cyrl)
type: mteb/flores
config: lit_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.33201581027669
- type: f1
value: 96.62384716732542
- type: main_score
value: 96.62384716732542
- type: precision
value: 96.3175230566535
- type: recall
value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nob_Latn-rus_Cyrl)
type: mteb/flores
config: nob_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.71541501976284
- type: f1
value: 98.30368906455863
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value: 98.30368906455863
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value: 98.10606060606061
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value: 98.71541501976284
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (scn_Latn-rus_Cyrl)
type: mteb/flores
config: scn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 67.95229103411222
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value: 70.45454545454545
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tgk_Cyrl-rus_Cyrl)
type: mteb/flores
config: tgk_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.4901185770751
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (yor_Latn-rus_Cyrl)
type: mteb/flores
config: yor_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 63.450823399938685
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value: 67.98418972332016
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (arz_Arab-rus_Cyrl)
type: mteb/flores
config: arz_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.56521739130434
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (cjk_Latn-rus_Cyrl)
type: mteb/flores
config: cjk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.63636363636363
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (gla_Latn-rus_Cyrl)
type: mteb/flores
config: gla_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 64.89369649409694
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value: 69.26877470355731
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kan_Knda-rus_Cyrl)
type: mteb/flores
config: kan_Knda-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (lmo_Latn-rus_Cyrl)
type: mteb/flores
config: lmo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 70.26320288266223
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value: 73.3201581027668
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (npi_Deva-rus_Cyrl)
type: mteb/flores
config: npi_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (shn_Mymr-rus_Cyrl)
type: mteb/flores
config: shn_Mymr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 36.56918548278505
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value: 39.426877470355734
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tgl_Latn-rus_Cyrl)
type: mteb/flores
config: tgl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.92490118577075
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (yue_Hant-rus_Cyrl)
type: mteb/flores
config: yue_Hant-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (asm_Beng-rus_Cyrl)
type: mteb/flores
config: asm_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.38292866553736
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value: 92.78656126482213
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ckb_Arab-rus_Cyrl)
type: mteb/flores
config: ckb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 78.5622171683459
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value: 80.8300395256917
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (gle_Latn-rus_Cyrl)
type: mteb/flores
config: gle_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.86956521739131
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value: 83.8774340026703
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value: 85.86956521739131
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kas_Arab-rus_Cyrl)
type: mteb/flores
config: kas_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.2491277759584
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value: 76.28458498023716
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ltg_Latn-rus_Cyrl)
type: mteb/flores
config: ltg_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 71.14624505928853
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value: 67.8135329666459
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value: 71.14624505928853
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nso_Latn-rus_Cyrl)
type: mteb/flores
config: nso_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.32513873917036
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value: 87.64822134387352
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sin_Sinh-rus_Cyrl)
type: mteb/flores
config: sin_Sinh-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.62845849802372
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value: 96.87986585219788
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value: 97.62845849802372
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tha_Thai-rus_Cyrl)
type: mteb/flores
config: tha_Thai-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.07312252964427
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zho_Hans-rus_Cyrl)
type: mteb/flores
config: zho_Hans-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.51778656126481
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ast_Latn-rus_Cyrl)
type: mteb/flores
config: ast_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.65217391304348
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value: 94.90649683857505
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value: 94.61352657004831
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value: 95.65217391304348
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (crh_Latn-rus_Cyrl)
type: mteb/flores
config: crh_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.08300395256917
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value: 92.20988998886428
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value: 91.85631013694254
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value: 93.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (glg_Latn-rus_Cyrl)
type: mteb/flores
config: glg_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.55335968379447
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value: 95.18006148440931
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value: 95.06540560888386
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value: 95.55335968379447
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kas_Deva-rus_Cyrl)
type: mteb/flores
config: kas_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 55.03952569169961
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value: 51.17660971469557
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value: 55.03952569169961
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ltz_Latn-rus_Cyrl)
type: mteb/flores
config: ltz_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.64822134387352
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value: 86.64179841897234
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value: 86.30023235431587
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value: 87.64822134387352
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nus_Latn-rus_Cyrl)
type: mteb/flores
config: nus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 27.4703557312253
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value: 25.703014277858088
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value: 25.703014277858088
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value: 25.194105476917315
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value: 27.4703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (slk_Latn-rus_Cyrl)
type: mteb/flores
config: slk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
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value: 99.1106719367589
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value: 99.1106719367589
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value: 99.02832674571805
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value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tir_Ethi-rus_Cyrl)
type: mteb/flores
config: tir_Ethi-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.73122529644269
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value: 78.66903754775608
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value: 78.66903754775608
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value: 77.86431694163612
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value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zho_Hant-rus_Cyrl)
type: mteb/flores
config: zho_Hant-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.22134387351778
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value: 97.66798418972333
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value: 97.66798418972333
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value: 97.40612648221344
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value: 98.22134387351778
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (awa_Deva-rus_Cyrl)
type: mteb/flores
config: awa_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.5296442687747
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value: 96.94224857268335
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value: 96.94224857268335
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value: 96.68560606060606
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value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cym_Latn-rus_Cyrl)
type: mteb/flores
config: cym_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.68774703557312
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value: 91.69854302097961
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value: 91.69854302097961
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value: 91.31236846157795
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value: 92.68774703557312
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (grn_Latn-rus_Cyrl)
type: mteb/flores
config: grn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 64.13043478260869
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value: 61.850586118740004
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value: 61.850586118740004
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value: 61.0049495186209
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value: 64.13043478260869
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kat_Geor-rus_Cyrl)
type: mteb/flores
config: kat_Geor-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.59881422924902
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value: 97.59881422924902
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value: 97.42534036012296
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lua_Latn-rus_Cyrl)
type: mteb/flores
config: lua_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 63.63636363636363
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value: 60.9709122526128
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value: 60.9709122526128
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value: 60.03915902282226
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value: 63.63636363636363
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nya_Latn-rus_Cyrl)
type: mteb/flores
config: nya_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.2292490118577
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value: 87.59723824473149
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value: 87.59723824473149
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value: 86.90172707867349
- type: recall
value: 89.2292490118577
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (slv_Latn-rus_Cyrl)
type: mteb/flores
config: slv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.74835309617917
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value: 98.74835309617917
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value: 98.63636363636364
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tpi_Latn-rus_Cyrl)
type: mteb/flores
config: tpi_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 77.37154150197628
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value: 75.44251611276084
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value: 75.44251611276084
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value: 74.78103665109595
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value: 77.37154150197628
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zsm_Latn-rus_Cyrl)
type: mteb/flores
config: zsm_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.96245059288538
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value: 98.96245059288538
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value: 98.8471673254282
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ayr_Latn-rus_Cyrl)
type: mteb/flores
config: ayr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 27.766798418972332
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value: 26.439103195281312
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value: 26.439103195281312
- type: precision
value: 26.052655604573964
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value: 27.766798418972332
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dan_Latn-rus_Cyrl)
type: mteb/flores
config: dan_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.07773386034255
- type: main_score
value: 99.07773386034255
- type: precision
value: 98.96245059288538
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (guj_Gujr-rus_Cyrl)
type: mteb/flores
config: guj_Gujr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.26449275362317
- type: main_score
value: 97.26449275362317
- type: precision
value: 97.02498588368154
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kaz_Cyrl-rus_Cyrl)
type: mteb/flores
config: kaz_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.03557312252964
- type: main_score
value: 97.03557312252964
- type: precision
value: 96.85022158342316
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lug_Latn-rus_Cyrl)
type: mteb/flores
config: lug_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.57707509881423
- type: f1
value: 65.93361605820395
- type: main_score
value: 65.93361605820395
- type: precision
value: 64.90348248593789
- type: recall
value: 68.57707509881423
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (oci_Latn-rus_Cyrl)
type: mteb/flores
config: oci_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.26482213438736
- type: f1
value: 85.33176417155623
- type: main_score
value: 85.33176417155623
- type: precision
value: 85.00208833384637
- type: recall
value: 86.26482213438736
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (smo_Latn-rus_Cyrl)
type: mteb/flores
config: smo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 77.96442687747036
- type: f1
value: 75.70960450188885
- type: main_score
value: 75.70960450188885
- type: precision
value: 74.8312632736777
- type: recall
value: 77.96442687747036
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tsn_Latn-rus_Cyrl)
type: mteb/flores
config: tsn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 84.38735177865613
- type: f1
value: 82.13656376349225
- type: main_score
value: 82.13656376349225
- type: precision
value: 81.16794543904518
- type: recall
value: 84.38735177865613
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zul_Latn-rus_Cyrl)
type: mteb/flores
config: zul_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 90.21739130434783
- type: f1
value: 88.77570602050753
- type: main_score
value: 88.77570602050753
- type: precision
value: 88.15978104021582
- type: recall
value: 90.21739130434783
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (azb_Arab-rus_Cyrl)
type: mteb/flores
config: azb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 65.71146245059289
- type: f1
value: 64.18825390221271
- type: main_score
value: 64.18825390221271
- type: precision
value: 63.66811154793568
- type: recall
value: 65.71146245059289
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (deu_Latn-rus_Cyrl)
type: mteb/flores
config: deu_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.70355731225297
- type: f1
value: 99.60474308300395
- type: main_score
value: 99.60474308300395
- type: precision
value: 99.55533596837944
- type: recall
value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hat_Latn-rus_Cyrl)
type: mteb/flores
config: hat_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 86.7588932806324
- type: f1
value: 85.86738623695146
- type: main_score
value: 85.86738623695146
- type: precision
value: 85.55235467420822
- type: recall
value: 86.7588932806324
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kbp_Latn-rus_Cyrl)
type: mteb/flores
config: kbp_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 34.88142292490119
- type: f1
value: 32.16511669463015
- type: main_score
value: 32.16511669463015
- type: precision
value: 31.432098549546318
- type: recall
value: 34.88142292490119
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (luo_Latn-rus_Cyrl)
type: mteb/flores
config: luo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 52.27272727272727
- type: f1
value: 49.60489626836975
- type: main_score
value: 49.60489626836975
- type: precision
value: 48.69639631803339
- type: recall
value: 52.27272727272727
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ory_Orya-rus_Cyrl)
type: mteb/flores
config: ory_Orya-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.82608695652173
- type: f1
value: 97.27437417654808
- type: main_score
value: 97.27437417654808
- type: precision
value: 97.04968944099377
- type: recall
value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sna_Latn-rus_Cyrl)
type: mteb/flores
config: sna_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 85.37549407114624
- type: f1
value: 83.09911316305177
- type: main_score
value: 83.09911316305177
- type: precision
value: 82.1284950958864
- type: recall
value: 85.37549407114624
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tso_Latn-rus_Cyrl)
type: mteb/flores
config: tso_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 82.90513833992095
- type: f1
value: 80.28290385503824
- type: main_score
value: 80.28290385503824
- type: precision
value: 79.23672543237761
- type: recall
value: 82.90513833992095
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (azj_Latn-rus_Cyrl)
type: mteb/flores
config: azj_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.49200075287031
- type: main_score
value: 97.49200075287031
- type: precision
value: 97.266139657444
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dik_Latn-rus_Cyrl)
type: mteb/flores
config: dik_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 38.43873517786561
- type: f1
value: 35.78152442955223
- type: main_score
value: 35.78152442955223
- type: precision
value: 34.82424325078237
- type: recall
value: 38.43873517786561
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hau_Latn-rus_Cyrl)
type: mteb/flores
config: hau_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 81.42292490118577
- type: f1
value: 79.24612283124593
- type: main_score
value: 79.24612283124593
- type: precision
value: 78.34736070751448
- type: recall
value: 81.42292490118577
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kea_Latn-rus_Cyrl)
type: mteb/flores
config: kea_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 81.62055335968378
- type: f1
value: 80.47015182884748
- type: main_score
value: 80.47015182884748
- type: precision
value: 80.02671028885862
- type: recall
value: 81.62055335968378
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lus_Latn-rus_Cyrl)
type: mteb/flores
config: lus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 62.74703557312253
- type: f1
value: 60.53900079111122
- type: main_score
value: 60.53900079111122
- type: precision
value: 59.80024202850289
- type: recall
value: 62.74703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pag_Latn-rus_Cyrl)
type: mteb/flores
config: pag_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 74.01185770750988
- type: f1
value: 72.57280648279529
- type: main_score
value: 72.57280648279529
- type: precision
value: 71.99952968456789
- type: recall
value: 74.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (snd_Arab-rus_Cyrl)
type: mteb/flores
config: snd_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.30434782608695
- type: f1
value: 90.24653499445358
- type: main_score
value: 90.24653499445358
- type: precision
value: 89.83134068200232
- type: recall
value: 91.30434782608695
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tuk_Latn-rus_Cyrl)
type: mteb/flores
config: tuk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 47.62845849802372
- type: f1
value: 45.812928836644254
- type: main_score
value: 45.812928836644254
- type: precision
value: 45.23713833170355
- type: recall
value: 47.62845849802372
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bak_Cyrl-rus_Cyrl)
type: mteb/flores
config: bak_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.8498023715415
- type: f1
value: 95.18904459615922
- type: main_score
value: 95.18904459615922
- type: precision
value: 94.92812441182006
- type: recall
value: 95.8498023715415
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dyu_Latn-rus_Cyrl)
type: mteb/flores
config: dyu_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 27.287335193938166
- type: main_score
value: 27.287335193938166
- type: precision
value: 26.583996026587492
- type: recall
value: 29.64426877470356
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (heb_Hebr-rus_Cyrl)
type: mteb/flores
config: heb_Hebr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (khk_Cyrl-rus_Cyrl)
type: mteb/flores
config: khk_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.15810276679841
- type: f1
value: 94.44009547764487
- type: main_score
value: 94.44009547764487
- type: precision
value: 94.16579797014579
- type: recall
value: 95.15810276679841
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lvs_Latn-rus_Cyrl)
type: mteb/flores
config: lvs_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.51467241585817
- type: main_score
value: 97.51467241585817
- type: precision
value: 97.36166007905138
- type: recall
value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pan_Guru-rus_Cyrl)
type: mteb/flores
config: pan_Guru-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.42918313570486
- type: main_score
value: 97.42918313570486
- type: precision
value: 97.22261434217955
- type: recall
value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (som_Latn-rus_Cyrl)
type: mteb/flores
config: som_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 75.69169960474308
- type: f1
value: 73.7211667065916
- type: main_score
value: 73.7211667065916
- type: precision
value: 72.95842401892384
- type: recall
value: 75.69169960474308
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tum_Latn-rus_Cyrl)
type: mteb/flores
config: tum_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 85.67193675889328
- type: f1
value: 82.9296066252588
- type: main_score
value: 82.9296066252588
- type: precision
value: 81.77330225447936
- type: recall
value: 85.67193675889328
- task:
type: Classification
dataset:
name: MTEB GeoreviewClassification (default)
type: ai-forever/georeview-classification
config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
metrics:
- type: accuracy
value: 44.6630859375
- type: f1
value: 42.607425073610536
- type: f1_weighted
value: 42.60639474586065
- type: main_score
value: 44.6630859375
- task:
type: Clustering
dataset:
name: MTEB GeoreviewClusteringP2P (default)
type: ai-forever/georeview-clustering-p2p
config: default
split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
metrics:
- type: main_score
value: 58.15951247070825
- type: v_measure
value: 58.15951247070825
- type: v_measure_std
value: 0.6739615788288809
- task:
type: Classification
dataset:
name: MTEB HeadlineClassification (default)
type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
- type: accuracy
value: 73.935546875
- type: f1
value: 73.8654872186846
- type: f1_weighted
value: 73.86733122685095
- type: main_score
value: 73.935546875
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification (default)
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 59.16015624999999
- type: ap
value: 55.52276605836938
- type: ap_weighted
value: 55.52276605836938
- type: f1
value: 58.614248199637956
- type: f1_weighted
value: 58.614248199637956
- type: main_score
value: 59.16015624999999
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification (default)
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 49.959999999999994
- type: f1
value: 48.4900332316098
- type: f1_weighted
value: 48.4900332316098
- type: main_score
value: 49.959999999999994
- task:
type: Classification
dataset:
name: MTEB LanguageClassification (default)
type: papluca/language-identification
config: default
split: test
revision: aa56583bf2bc52b0565770607d6fc3faebecf9e2
metrics:
- type: accuracy
value: 71.005859375
- type: f1
value: 69.63481100303348
- type: f1_weighted
value: 69.64640413409529
- type: main_score
value: 71.005859375
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (ru)
type: reciTAL/mlsum
config: ru
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: main_score
value: 42.11280087032343
- type: v_measure
value: 42.11280087032343
- type: v_measure_std
value: 6.7619971723605135
- type: main_score
value: 43.00112546945811
- type: v_measure
value: 43.00112546945811
- type: v_measure_std
value: 1.4740560414835675
- type: main_score
value: 39.81446080575161
- type: v_measure
value: 39.81446080575161
- type: v_measure_std
value: 7.125661320308298
- type: main_score
value: 39.29659668980239
- type: v_measure
value: 39.29659668980239
- type: v_measure_std
value: 2.6570502923023094
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ru)
type: Shitao/MLDR
config: ru
split: dev
revision: d67138e705d963e346253a80e59676ddb418810a
metrics:
- type: main_score
value: 38.671
- type: map_at_1
value: 30.0
- type: map_at_10
value: 36.123
- type: map_at_100
value: 36.754999999999995
- type: map_at_1000
value: 36.806
- type: map_at_20
value: 36.464
- type: map_at_3
value: 35.25
- type: map_at_5
value: 35.8
- type: mrr_at_1
value: 30.0
- type: mrr_at_10
value: 36.122817460317464
- type: mrr_at_100
value: 36.75467016625293
- type: mrr_at_1000
value: 36.80612724920882
- type: mrr_at_20
value: 36.46359681984682
- type: mrr_at_3
value: 35.25
- type: mrr_at_5
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type: Classification
dataset:
name: MTEB MultilingualSentimentClassification (rus)
type: mteb/multilingual-sentiment-classification
config: rus
split: test
revision: 2b9b4d10fc589af67794141fe8cbd3739de1eb33
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (arb_Arab-rus_Cyrl)
type: mteb/NTREX
config: arb_Arab-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
name: MTEB NTREXBitextMining (bel_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: bel_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
name: MTEB NTREXBitextMining (ben_Beng-rus_Cyrl)
type: mteb/NTREX
config: ben_Beng-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
name: MTEB NTREXBitextMining (bos_Latn-rus_Cyrl)
type: mteb/NTREX
config: bos_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
name: MTEB NTREXBitextMining (bul_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: bul_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
name: MTEB NTREXBitextMining (ces_Latn-rus_Cyrl)
type: mteb/NTREX
config: ces_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
name: MTEB NTREXBitextMining (deu_Latn-rus_Cyrl)
type: mteb/NTREX
config: deu_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
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type: mteb/NTREX
config: ell_Grek-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
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type: mteb/NTREX
config: eng_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
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type: mteb/NTREX
config: fas_Arab-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
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type: mteb/NTREX
config: fin_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
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type: mteb/NTREX
config: fra_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
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type: mteb/NTREX
config: heb_Hebr-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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dataset:
name: MTEB NTREXBitextMining (hin_Deva-rus_Cyrl)
type: mteb/NTREX
config: hin_Deva-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (hrv_Latn-rus_Cyrl)
type: mteb/NTREX
config: hrv_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (hun_Latn-rus_Cyrl)
type: mteb/NTREX
config: hun_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
name: MTEB NTREXBitextMining (ind_Latn-rus_Cyrl)
type: mteb/NTREX
config: ind_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (jpn_Jpan-rus_Cyrl)
type: mteb/NTREX
config: jpn_Jpan-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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dataset:
name: MTEB NTREXBitextMining (kor_Hang-rus_Cyrl)
type: mteb/NTREX
config: kor_Hang-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (lit_Latn-rus_Cyrl)
type: mteb/NTREX
config: lit_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 90.28542814221332
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (mkd_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: mkd_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (nld_Latn-rus_Cyrl)
type: mteb/NTREX
config: nld_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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type: BitextMining
dataset:
name: MTEB NTREXBitextMining (pol_Latn-rus_Cyrl)
type: mteb/NTREX
config: pol_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
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config: slv_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.23835753630446
- type: f1
value: 90.5061759305625
- type: main_score
value: 90.5061759305625
- type: precision
value: 89.74231188051918
- type: recall
value: 92.23835753630446
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (spa_Latn-rus_Cyrl)
type: mteb/NTREX
config: spa_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 96.54481722583876
- type: f1
value: 95.54665331330328
- type: main_score
value: 95.54665331330328
- type: precision
value: 95.06342847604739
- type: recall
value: 96.54481722583876
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (srp_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: srp_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 83.62543815723585
- type: f1
value: 80.77095672699816
- type: main_score
value: 80.77095672699816
- type: precision
value: 79.74674313056886
- type: recall
value: 83.62543815723585
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (srp_Latn-rus_Cyrl)
type: mteb/NTREX
config: srp_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.44166249374061
- type: f1
value: 93.00733206591994
- type: main_score
value: 93.00733206591994
- type: precision
value: 92.37203026762366
- type: recall
value: 94.44166249374061
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (swa_Latn-rus_Cyrl)
type: mteb/NTREX
config: swa_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 90.23535302954431
- type: f1
value: 87.89596482636041
- type: main_score
value: 87.89596482636041
- type: precision
value: 86.87060227370694
- type: recall
value: 90.23535302954431
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (swe_Latn-rus_Cyrl)
type: mteb/NTREX
config: swe_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.44316474712068
- type: f1
value: 94.1896177599733
- type: main_score
value: 94.1896177599733
- type: precision
value: 93.61542313470206
- type: recall
value: 95.44316474712068
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (tam_Taml-rus_Cyrl)
type: mteb/NTREX
config: tam_Taml-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 89.68452679018529
- type: f1
value: 87.37341160650037
- type: main_score
value: 87.37341160650037
- type: precision
value: 86.38389402285247
- type: recall
value: 89.68452679018529
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (tur_Latn-rus_Cyrl)
type: mteb/NTREX
config: tur_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 93.89083625438157
- type: f1
value: 92.33892505424804
- type: main_score
value: 92.33892505424804
- type: precision
value: 91.63125640842216
- type: recall
value: 93.89083625438157
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (ukr_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: ukr_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 96.14421632448673
- type: f1
value: 95.11028447433054
- type: main_score
value: 95.11028447433054
- type: precision
value: 94.62944416624937
- type: recall
value: 96.14421632448673
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (vie_Latn-rus_Cyrl)
type: mteb/NTREX
config: vie_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 93.79068602904357
- type: f1
value: 92.14989150392256
- type: main_score
value: 92.14989150392256
- type: precision
value: 91.39292271740945
- type: recall
value: 93.79068602904357
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (zho_Hant-rus_Cyrl)
type: mteb/NTREX
config: zho_Hant-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 89.13370055082625
- type: f1
value: 86.51514618639217
- type: main_score
value: 86.51514618639217
- type: precision
value: 85.383920035898
- type: recall
value: 89.13370055082625
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (zul_Latn-rus_Cyrl)
type: mteb/NTREX
config: zul_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 81.17175763645467
- type: f1
value: 77.72331766047338
- type: main_score
value: 77.72331766047338
- type: precision
value: 76.24629555848075
- type: recall
value: 81.17175763645467
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (ru)
type: GEM/opusparcus
config: ru
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
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value: 73.09136420525657
- type: cosine_accuracy_threshold
value: 87.70400881767273
- type: cosine_ap
value: 86.51938550599533
- type: cosine_f1
value: 80.84358523725834
- type: cosine_f1_threshold
value: 86.90648078918457
- type: cosine_precision
value: 73.24840764331209
- type: cosine_recall
value: 90.19607843137256
- type: dot_accuracy
value: 73.09136420525657
- type: dot_accuracy_threshold
value: 87.7040147781372
- type: dot_ap
value: 86.51934769946833
- type: dot_f1
value: 80.84358523725834
- type: dot_f1_threshold
value: 86.90648078918457
- type: dot_precision
value: 73.24840764331209
- type: dot_recall
value: 90.19607843137256
- type: euclidean_accuracy
value: 73.09136420525657
- type: euclidean_accuracy_threshold
value: 49.590304493904114
- type: euclidean_ap
value: 86.51934769946833
- type: euclidean_f1
value: 80.84358523725834
- type: euclidean_f1_threshold
value: 51.173269748687744
- type: euclidean_precision
value: 73.24840764331209
- type: euclidean_recall
value: 90.19607843137256
- type: main_score
value: 86.51976811057995
- type: manhattan_accuracy
value: 73.40425531914893
- type: manhattan_accuracy_threshold
value: 757.8278541564941
- type: manhattan_ap
value: 86.51976811057995
- type: manhattan_f1
value: 80.92898615453328
- type: manhattan_f1_threshold
value: 778.3821105957031
- type: manhattan_precision
value: 74.32321575061526
- type: manhattan_recall
value: 88.8235294117647
- type: max_ap
value: 86.51976811057995
- type: max_f1
value: 80.92898615453328
- type: max_precision
value: 74.32321575061526
- type: max_recall
value: 90.19607843137256
- type: similarity_accuracy
value: 73.09136420525657
- type: similarity_accuracy_threshold
value: 87.70400881767273
- type: similarity_ap
value: 86.51938550599533
- type: similarity_f1
value: 80.84358523725834
- type: similarity_f1_threshold
value: 86.90648078918457
- type: similarity_precision
value: 73.24840764331209
- type: similarity_recall
value: 90.19607843137256
- task:
type: Retrieval
dataset:
name: MTEB PublicHealthQA (russian)
type: xhluca/publichealth-qa
config: russian
split: test
revision: main
metrics:
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value: 79.303
- type: map_at_1
value: 61.538000000000004
- type: map_at_10
value: 74.449
- type: map_at_100
value: 74.687
- type: map_at_1000
value: 74.687
- type: map_at_20
value: 74.589
- type: map_at_3
value: 73.333
- type: map_at_5
value: 74.256
- type: mrr_at_1
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value: 74.68730304304074
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- type: mrr_at_20
value: 74.58857808857809
- type: mrr_at_3
value: 73.33333333333333
- type: mrr_at_5
value: 74.25641025641025
- type: nauc_map_at_1000_diff1
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value: 41.735794471409015
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value: 42.243121474939365
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value: 32.38542979413408
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value: 41.94787773313971
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value: 61.40157474408937
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value: 51.47230077853947
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value: 42.63540269440141
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value: 61.07631147583098
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value: 52.02626939341523
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value: 42.511607332150334
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value: 51.37093181241067
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value: 41.735794471409015
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value: 51.47230077853947
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value: 45.39305589413174
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value: 54.23992718744033
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value: 58.70739588066225
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value: 47.10553115762958
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value: 100.0
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value: 100.0
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value: .nan
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value: 86.45729478231968
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value: 83.74875373878356
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value: .nan
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value: .nan
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value: .nan
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value: .nan
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value: .nan
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value: 35.72622112397516
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value: 89.84297108673968
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value: 86.60269192422749
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value: 66.39100974909151
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value: 44.77165601342703
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value: 32.38542979413408
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value: 29.188449183726323
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value: 86.45729478231985
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value: 86.45729478231985
- type: nauc_recall_at_3_diff1
value: 50.29412662923603
- type: nauc_recall_at_3_max
value: 68.98223127174562
- type: nauc_recall_at_3_std
value: 70.31195520376346
- type: nauc_recall_at_5_diff1
value: 39.64888428812445
- type: nauc_recall_at_5_max
value: 86.34097706879359
- type: nauc_recall_at_5_std
value: 83.74875373878366
- type: ndcg_at_1
value: 61.538000000000004
- type: ndcg_at_10
value: 79.303
- type: ndcg_at_100
value: 80.557
- type: ndcg_at_1000
value: 80.557
- type: ndcg_at_20
value: 79.732
- type: ndcg_at_3
value: 77.033
- type: ndcg_at_5
value: 78.818
- type: precision_at_1
value: 61.538000000000004
- type: precision_at_10
value: 9.385
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.769
- type: precision_at_3
value: 29.231
- type: precision_at_5
value: 18.462
- type: recall_at_1
value: 61.538000000000004
- type: recall_at_10
value: 93.84599999999999
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 95.38499999999999
- type: recall_at_3
value: 87.69200000000001
- type: recall_at_5
value: 92.308
- task:
type: STS
dataset:
name: MTEB RUParaPhraserSTS (default)
type: merionum/ru_paraphraser
config: default
split: test
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
metrics:
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value: 64.73554596215753
- type: cosine_spearman
value: 70.45849652271855
- type: euclidean_pearson
value: 68.08069844834267
- type: euclidean_spearman
value: 70.45854872959124
- type: main_score
value: 70.45849652271855
- type: manhattan_pearson
value: 67.88325986519624
- type: manhattan_spearman
value: 70.21131896834542
- type: pearson
value: 64.73554596215753
- type: spearman
value: 70.45849652271855
- task:
type: Retrieval
dataset:
name: MTEB RiaNewsRetrieval (default)
type: ai-forever/ria-news-retrieval
config: default
split: test
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
metrics:
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value: 70.00999999999999
- type: map_at_1
value: 55.97
- type: map_at_10
value: 65.59700000000001
- type: map_at_100
value: 66.057
- type: map_at_1000
value: 66.074
- type: map_at_20
value: 65.892
- type: map_at_3
value: 63.74999999999999
- type: map_at_5
value: 64.84299999999999
- type: mrr_at_1
value: 55.88999999999999
- type: mrr_at_10
value: 65.55873015872977
- type: mrr_at_100
value: 66.01891495129716
- type: mrr_at_1000
value: 66.03538391493299
- type: mrr_at_20
value: 65.85351193431555
- type: mrr_at_3
value: 63.7133333333329
- type: mrr_at_5
value: 64.80483333333268
- type: nauc_map_at_1000_diff1
value: 65.95332946436318
- type: nauc_map_at_1000_max
value: 28.21204156197811
- type: nauc_map_at_1000_std
value: -13.139245767083743
- type: nauc_map_at_100_diff1
value: 65.94763105024367
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value: 28.212832170078205
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value: -13.131425849370665
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value: 65.88455089448388
- type: nauc_map_at_10_max
value: 28.13555838776792
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value: -13.326989827081023
- type: nauc_map_at_1_diff1
value: 69.31275711813979
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value: 26.386708520283758
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value: -14.434616447245464
- type: nauc_map_at_20_diff1
value: 65.91227032605677
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value: 28.20538655600886
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value: -13.191148834410274
- type: nauc_map_at_3_diff1
value: 66.0051677952641
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value: 28.25443420019022
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value: -13.893284109029558
- type: nauc_map_at_5_diff1
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dataset:
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type: ai-forever/rubq-reranking
config: default
split: test
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
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type: ai-forever/rubq-retrieval
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split: test
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type: Classification
dataset:
name: MTEB RuReviewsClassification (default)
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
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dataset:
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type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
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dataset:
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type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
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dataset:
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type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
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dataset:
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type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
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dataset:
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type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
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dataset:
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type: mteb/sib200
config: rus_Cyrl
split: test
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
metrics:
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value: 66.43317771876966
- type: main_score
value: 66.36363636363637
- task:
type: Clustering
dataset:
name: MTEB SIB200ClusteringS2S (rus_Cyrl)
type: mteb/sib200
config: rus_Cyrl
split: test
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
metrics:
- type: main_score
value: 33.99178497314711
- type: v_measure
value: 33.99178497314711
- type: v_measure_std
value: 4.036337464043786
- task:
type: STS
dataset:
name: MTEB STS22.v2 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (ru)
type: mteb/stsb_multi_mt
config: ru
split: dev
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 78.43928769569945
- type: cosine_spearman
value: 78.23961768018884
- type: euclidean_pearson
value: 77.4718694027985
- type: euclidean_spearman
value: 78.23887044760475
- type: main_score
value: 78.23961768018884
- type: manhattan_pearson
value: 77.34517128089547
- type: manhattan_spearman
value: 78.1146477340426
- type: pearson
value: 78.43928769569945
- type: spearman
value: 78.23961768018884
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification (default)
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 22.8125
- type: f1
value: 17.31969589593409
- type: lrap
value: 33.82412380642287
- type: main_score
value: 22.8125
- task:
type: PairClassification
dataset:
name: MTEB TERRa (default)
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
- type: cosine_accuracy
value: 57.32899022801303
- type: cosine_accuracy_threshold
value: 85.32201051712036
- type: cosine_ap
value: 55.14264553720072
- type: cosine_f1
value: 66.83544303797468
- type: cosine_f1_threshold
value: 85.32201051712036
- type: cosine_precision
value: 54.54545454545454
- type: cosine_recall
value: 86.27450980392157
- type: dot_accuracy
value: 57.32899022801303
- type: dot_accuracy_threshold
value: 85.32201051712036
- type: dot_ap
value: 55.14264553720072
- type: dot_f1
value: 66.83544303797468
- type: dot_f1_threshold
value: 85.32201051712036
- type: dot_precision
value: 54.54545454545454
- type: dot_recall
value: 86.27450980392157
- type: euclidean_accuracy
value: 57.32899022801303
- type: euclidean_accuracy_threshold
value: 54.18117046356201
- type: euclidean_ap
value: 55.14264553720072
- type: euclidean_f1
value: 66.83544303797468
- type: euclidean_f1_threshold
value: 54.18117046356201
- type: euclidean_precision
value: 54.54545454545454
- type: euclidean_recall
value: 86.27450980392157
- type: main_score
value: 55.14264553720072
- type: manhattan_accuracy
value: 57.32899022801303
- type: manhattan_accuracy_threshold
value: 828.8480758666992
- type: manhattan_ap
value: 55.077974053622555
- type: manhattan_f1
value: 66.82352941176471
- type: manhattan_f1_threshold
value: 885.6784820556641
- type: manhattan_precision
value: 52.20588235294118
- type: manhattan_recall
value: 92.81045751633987
- type: max_ap
value: 55.14264553720072
- type: max_f1
value: 66.83544303797468
- type: max_precision
value: 54.54545454545454
- type: max_recall
value: 92.81045751633987
- type: similarity_accuracy
value: 57.32899022801303
- type: similarity_accuracy_threshold
value: 85.32201051712036
- type: similarity_ap
value: 55.14264553720072
- type: similarity_f1
value: 66.83544303797468
- type: similarity_f1_threshold
value: 85.32201051712036
- type: similarity_precision
value: 54.54545454545454
- type: similarity_recall
value: 86.27450980392157
- task:
type: PairClassification
dataset:
name: MTEB XNLI (ru)
type: mteb/xnli
config: ru
split: test
revision: 09698e0180d87dc247ca447d3a1248b931ac0cdb
metrics:
- type: cosine_accuracy
value: 67.6923076923077
- type: cosine_accuracy_threshold
value: 87.6681923866272
- type: cosine_ap
value: 73.18693800863593
- type: cosine_f1
value: 70.40641099026904
- type: cosine_f1_threshold
value: 85.09706258773804
- type: cosine_precision
value: 57.74647887323944
- type: cosine_recall
value: 90.17595307917888
- type: dot_accuracy
value: 67.6923076923077
- type: dot_accuracy_threshold
value: 87.66818642616272
- type: dot_ap
value: 73.18693800863593
- type: dot_f1
value: 70.40641099026904
- type: dot_f1_threshold
value: 85.09706258773804
- type: dot_precision
value: 57.74647887323944
- type: dot_recall
value: 90.17595307917888
- type: euclidean_accuracy
value: 67.6923076923077
- type: euclidean_accuracy_threshold
value: 49.662476778030396
- type: euclidean_ap
value: 73.18693800863593
- type: euclidean_f1
value: 70.40641099026904
- type: euclidean_f1_threshold
value: 54.59475517272949
- type: euclidean_precision
value: 57.74647887323944
- type: euclidean_recall
value: 90.17595307917888
- type: main_score
value: 73.18693800863593
- type: manhattan_accuracy
value: 67.54578754578755
- type: manhattan_accuracy_threshold
value: 777.1001815795898
- type: manhattan_ap
value: 72.98861474758783
- type: manhattan_f1
value: 70.6842435655995
- type: manhattan_f1_threshold
value: 810.3782653808594
- type: manhattan_precision
value: 61.80021953896817
- type: manhattan_recall
value: 82.55131964809385
- type: max_ap
value: 73.18693800863593
- type: max_f1
value: 70.6842435655995
- type: max_precision
value: 61.80021953896817
- type: max_recall
value: 90.17595307917888
- type: similarity_accuracy
value: 67.6923076923077
- type: similarity_accuracy_threshold
value: 87.6681923866272
- type: similarity_ap
value: 73.18693800863593
- type: similarity_f1
value: 70.40641099026904
- type: similarity_f1_threshold
value: 85.09706258773804
- type: similarity_precision
value: 57.74647887323944
- type: similarity_recall
value: 90.17595307917888
- task:
type: PairClassification
dataset:
name: MTEB XNLIV2 (russian)
type: mteb/xnli2.0-multi-pair
config: russian
split: test
revision: 5b7d477a8c62cdd18e2fed7e015497c20b4371ad
metrics:
- type: cosine_accuracy
value: 68.35164835164835
- type: cosine_accuracy_threshold
value: 88.48621845245361
- type: cosine_ap
value: 73.10205506215699
- type: cosine_f1
value: 71.28712871287128
- type: cosine_f1_threshold
value: 87.00399398803711
- type: cosine_precision
value: 61.67023554603854
- type: cosine_recall
value: 84.4574780058651
- type: dot_accuracy
value: 68.35164835164835
- type: dot_accuracy_threshold
value: 88.48622441291809
- type: dot_ap
value: 73.10191110714706
- type: dot_f1
value: 71.28712871287128
- type: dot_f1_threshold
value: 87.00399398803711
- type: dot_precision
value: 61.67023554603854
- type: dot_recall
value: 84.4574780058651
- type: euclidean_accuracy
value: 68.35164835164835
- type: euclidean_accuracy_threshold
value: 47.98704385757446
- type: euclidean_ap
value: 73.10205506215699
- type: euclidean_f1
value: 71.28712871287128
- type: euclidean_f1_threshold
value: 50.982362031936646
- type: euclidean_precision
value: 61.67023554603854
- type: euclidean_recall
value: 84.4574780058651
- type: main_score
value: 73.10205506215699
- type: manhattan_accuracy
value: 67.91208791208791
- type: manhattan_accuracy_threshold
value: 746.1360931396484
- type: manhattan_ap
value: 72.8954736175069
- type: manhattan_f1
value: 71.1297071129707
- type: manhattan_f1_threshold
value: 808.0789566040039
- type: manhattan_precision
value: 60.04036326942482
- type: manhattan_recall
value: 87.2434017595308
- type: max_ap
value: 73.10205506215699
- type: max_f1
value: 71.28712871287128
- type: max_precision
value: 61.67023554603854
- type: max_recall
value: 87.2434017595308
- type: similarity_accuracy
value: 68.35164835164835
- type: similarity_accuracy_threshold
value: 88.48621845245361
- type: similarity_ap
value: 73.10205506215699
- type: similarity_f1
value: 71.28712871287128
- type: similarity_f1_threshold
value: 87.00399398803711
- type: similarity_precision
value: 61.67023554603854
- type: similarity_recall
value: 84.4574780058651
- task:
type: Retrieval
dataset:
name: MTEB XQuADRetrieval (ru)
type: google/xquad
config: ru
split: validation
revision: 51adfef1c1287aab1d2d91b5bead9bcfb9c68583
metrics:
- type: main_score
value: 95.705
- type: map_at_1
value: 90.802
- type: map_at_10
value: 94.427
- type: map_at_100
value: 94.451
- type: map_at_1000
value: 94.451
- type: map_at_20
value: 94.446
- type: map_at_3
value: 94.121
- type: map_at_5
value: 94.34
- type: mrr_at_1
value: 90.80168776371308
- type: mrr_at_10
value: 94.42659567343111
- type: mrr_at_100
value: 94.45099347521871
- type: mrr_at_1000
value: 94.45099347521871
- type: mrr_at_20
value: 94.44574530017569
- type: mrr_at_3
value: 94.12095639943743
- type: mrr_at_5
value: 94.34036568213786
- type: nauc_map_at_1000_diff1
value: 87.40573202946949
- type: nauc_map_at_1000_max
value: 65.56220344468791
- type: nauc_map_at_1000_std
value: 8.865583291735863
- type: nauc_map_at_100_diff1
value: 87.40573202946949
- type: nauc_map_at_100_max
value: 65.56220344468791
- type: nauc_map_at_100_std
value: 8.865583291735863
- type: nauc_map_at_10_diff1
value: 87.43657080570291
- type: nauc_map_at_10_max
value: 65.71295628534446
- type: nauc_map_at_10_std
value: 9.055399339099655
- type: nauc_map_at_1_diff1
value: 88.08395824560428
- type: nauc_map_at_1_max
value: 62.92813192908893
- type: nauc_map_at_1_std
value: 6.738987385482432
- type: nauc_map_at_20_diff1
value: 87.40979818966589
- type: nauc_map_at_20_max
value: 65.59474346926105
- type: nauc_map_at_20_std
value: 8.944420599300914
- type: nauc_map_at_3_diff1
value: 86.97771892161035
- type: nauc_map_at_3_max
value: 66.14330030122467
- type: nauc_map_at_3_std
value: 8.62516327793521
- type: nauc_map_at_5_diff1
value: 87.30273362211798
- type: nauc_map_at_5_max
value: 66.1522476584607
- type: nauc_map_at_5_std
value: 9.780940862679724
- type: nauc_mrr_at_1000_diff1
value: 87.40573202946949
- type: nauc_mrr_at_1000_max
value: 65.56220344468791
- type: nauc_mrr_at_1000_std
value: 8.865583291735863
- type: nauc_mrr_at_100_diff1
value: 87.40573202946949
- type: nauc_mrr_at_100_max
value: 65.56220344468791
- type: nauc_mrr_at_100_std
value: 8.865583291735863
- type: nauc_mrr_at_10_diff1
value: 87.43657080570291
- type: nauc_mrr_at_10_max
value: 65.71295628534446
- type: nauc_mrr_at_10_std
value: 9.055399339099655
- type: nauc_mrr_at_1_diff1
value: 88.08395824560428
- type: nauc_mrr_at_1_max
value: 62.92813192908893
- type: nauc_mrr_at_1_std
value: 6.738987385482432
- type: nauc_mrr_at_20_diff1
value: 87.40979818966589
- type: nauc_mrr_at_20_max
value: 65.59474346926105
- type: nauc_mrr_at_20_std
value: 8.944420599300914
- type: nauc_mrr_at_3_diff1
value: 86.97771892161035
- type: nauc_mrr_at_3_max
value: 66.14330030122467
- type: nauc_mrr_at_3_std
value: 8.62516327793521
- type: nauc_mrr_at_5_diff1
value: 87.30273362211798
- type: nauc_mrr_at_5_max
value: 66.1522476584607
- type: nauc_mrr_at_5_std
value: 9.780940862679724
- type: nauc_ndcg_at_1000_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_1000_max
value: 66.00874244792789
- type: nauc_ndcg_at_1000_std
value: 9.479929342875067
- type: nauc_ndcg_at_100_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_100_max
value: 66.00874244792789
- type: nauc_ndcg_at_100_std
value: 9.479929342875067
- type: nauc_ndcg_at_10_diff1
value: 87.54508467181488
- type: nauc_ndcg_at_10_max
value: 66.88756470312894
- type: nauc_ndcg_at_10_std
value: 10.812624405397022
- type: nauc_ndcg_at_1_diff1
value: 88.08395824560428
- type: nauc_ndcg_at_1_max
value: 62.92813192908893
- type: nauc_ndcg_at_1_std
value: 6.738987385482432
- type: nauc_ndcg_at_20_diff1
value: 87.42097894104597
- type: nauc_ndcg_at_20_max
value: 66.37031898778943
- type: nauc_ndcg_at_20_std
value: 10.34862538094813
- type: nauc_ndcg_at_3_diff1
value: 86.50039907157999
- type: nauc_ndcg_at_3_max
value: 67.97798288917929
- type: nauc_ndcg_at_3_std
value: 10.162410286746852
- type: nauc_ndcg_at_5_diff1
value: 87.13322094568531
- type: nauc_ndcg_at_5_max
value: 68.08576118683821
- type: nauc_ndcg_at_5_std
value: 12.639637379592855
- type: nauc_precision_at_1000_diff1
value: 100.0
- type: nauc_precision_at_1000_max
value: 100.0
- type: nauc_precision_at_1000_std
value: 100.0
- type: nauc_precision_at_100_diff1
value: 100.0
- type: nauc_precision_at_100_max
value: 100.0
- type: nauc_precision_at_100_std
value: 100.0
- type: nauc_precision_at_10_diff1
value: 93.46711505595813
- type: nauc_precision_at_10_max
value: 100.0
- type: nauc_precision_at_10_std
value: 65.42573557179935
- type: nauc_precision_at_1_diff1
value: 88.08395824560428
- type: nauc_precision_at_1_max
value: 62.92813192908893
- type: nauc_precision_at_1_std
value: 6.738987385482432
- type: nauc_precision_at_20_diff1
value: 91.28948674127133
- type: nauc_precision_at_20_max
value: 100.0
- type: nauc_precision_at_20_std
value: 90.74278258632364
- type: nauc_precision_at_3_diff1
value: 82.64606115071832
- type: nauc_precision_at_3_max
value: 83.26201582412921
- type: nauc_precision_at_3_std
value: 23.334013491433762
- type: nauc_precision_at_5_diff1
value: 85.0867539350284
- type: nauc_precision_at_5_max
value: 96.57011448655484
- type: nauc_precision_at_5_std
value: 56.46869543426768
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: .nan
- type: nauc_recall_at_100_max
value: .nan
- type: nauc_recall_at_100_std
value: .nan
- type: nauc_recall_at_10_diff1
value: 93.46711505595623
- type: nauc_recall_at_10_max
value: 100.0
- type: nauc_recall_at_10_std
value: 65.42573557180279
- type: nauc_recall_at_1_diff1
value: 88.08395824560428
- type: nauc_recall_at_1_max
value: 62.92813192908893
- type: nauc_recall_at_1_std
value: 6.738987385482432
- type: nauc_recall_at_20_diff1
value: 91.28948674127474
- type: nauc_recall_at_20_max
value: 100.0
- type: nauc_recall_at_20_std
value: 90.74278258632704
- type: nauc_recall_at_3_diff1
value: 82.64606115071967
- type: nauc_recall_at_3_max
value: 83.26201582413023
- type: nauc_recall_at_3_std
value: 23.334013491434007
- type: nauc_recall_at_5_diff1
value: 85.08675393502854
- type: nauc_recall_at_5_max
value: 96.57011448655487
- type: nauc_recall_at_5_std
value: 56.46869543426658
- type: ndcg_at_1
value: 90.802
- type: ndcg_at_10
value: 95.705
- type: ndcg_at_100
value: 95.816
- type: ndcg_at_1000
value: 95.816
- type: ndcg_at_20
value: 95.771
- type: ndcg_at_3
value: 95.11699999999999
- type: ndcg_at_5
value: 95.506
- type: precision_at_1
value: 90.802
- type: precision_at_10
value: 9.949
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.987
- type: precision_at_3
value: 32.658
- type: precision_at_5
value: 19.781000000000002
- type: recall_at_1
value: 90.802
- type: recall_at_10
value: 99.494
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.747
- type: recall_at_3
value: 97.975
- type: recall_at_5
value: 98.90299999999999
---
# thoddnn/multilingual-e5-small-4bit-mlx
The Model [thoddnn/multilingual-e5-small-4bit-mlx](https://huggingface.co/thoddnn/multilingual-e5-small-4bit-mlx) was converted to MLX format from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) using mlx-lm version **0.0.3**.
## Use with mlx
```bash
pip install mlx-embeddings
```
```python
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("thoddnn/multilingual-e5-small-4bit-mlx")
# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds # Normalized embeddings
# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)
print("Similarity matrix between texts:")
print(similarity_matrix)
```
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756160598
|
liukevin666
| 2025-08-25T22:24:17Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:24:11Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_058
|
AnonymousCS
| 2025-08-25T22:23:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T22:22:16Z
|
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_058
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_058
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7232
- Accuracy: 0.9023
- 1-f1: 0.3902
- 1-recall: 0.4848
- 1-precision: 0.3265
- Balanced Acc: 0.7080
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4767 | 1.0 | 32 | 0.4711 | 0.7031 | 0.2621 | 0.8182 | 0.1561 | 0.7567 |
| 0.4079 | 2.0 | 64 | 0.4756 | 0.7734 | 0.3095 | 0.7879 | 0.1926 | 0.7802 |
| 0.3112 | 3.0 | 96 | 0.7232 | 0.9023 | 0.3902 | 0.4848 | 0.3265 | 0.7080 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756160508
|
Dejiat
| 2025-08-25T22:22:17Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:22:14Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raposmans/blockassist-bc-tricky_padded_jellyfish_1756160425
|
raposmans
| 2025-08-25T22:21:11Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky padded jellyfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:20:48Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky padded jellyfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thoddnn/Qwen3-Embedding-0.6B-4bit-MLX
|
thoddnn
| 2025-08-25T22:20:41Z
| 0
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"qwen3",
"text-generation",
"transformers",
"sentence-similarity",
"feature-extraction",
"text-embeddings-inference",
"mlx",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-25T22:18:51Z
|
---
license: apache-2.0
base_model:
- Qwen/Qwen3-0.6B-Base
tags:
- transformers
- sentence-transformers
- sentence-similarity
- feature-extraction
- text-embeddings-inference
- mlx
---
# thoddnn/Qwen3-Embedding-0.6B-4bit-MLX
The Model [thoddnn/Qwen3-Embedding-0.6B-4bit-MLX](https://huggingface.co/thoddnn/Qwen3-Embedding-0.6B-4bit-MLX) was converted to MLX format from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) using mlx-lm version **0.0.3**.
## Use with mlx
```bash
pip install mlx-embeddings
```
```python
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("thoddnn/Qwen3-Embedding-0.6B-4bit-MLX")
# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds # Normalized embeddings
# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)
print("Similarity matrix between texts:")
print(similarity_matrix)
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756160270
|
Dejiat
| 2025-08-25T22:18:13Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:18:11Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Qwen2.5-7B-Instruct-t1_25k_v2_tag5_hermes
|
lemonhat
| 2025-08-25T22:17:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T22:06:34Z
|
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_25k_v2_tag5_hermes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t1_25k_v2_tag5_hermes
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the t1_25k_v2_tag5_hermes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3222 | 0.1408 | 100 | 0.3009 |
| 0.2607 | 0.2817 | 200 | 0.2794 |
| 0.2946 | 0.4225 | 300 | 0.2706 |
| 0.2429 | 0.5634 | 400 | 0.2605 |
| 0.2607 | 0.7042 | 500 | 0.2546 |
| 0.2383 | 0.8451 | 600 | 0.2517 |
| 0.2373 | 0.9859 | 700 | 0.2513 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
g-assismoraes/Qwen3-4B-Base-tile-perm-alpha0.8-var-hatebr
|
g-assismoraes
| 2025-08-25T22:17:32Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T22:06:52Z
|
---
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]
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756158595
|
capungmerah627
| 2025-08-25T22:16:56Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:16:53Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/balinese_tts_model-i1-GGUF
|
mradermacher
| 2025-08-25T22:16:27Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:1ndianajones/balinese_tts_model",
"base_model:quantized:1ndianajones/balinese_tts_model",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-25T21:42:54Z
|
---
base_model: 1ndianajones/balinese_tts_model
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/1ndianajones/balinese_tts_model
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#balinese_tts_model-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/balinese_tts_model-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ1_S.gguf) | i1-IQ1_S | 1.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ1_M.gguf) | i1-IQ1_M | 1.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ2_S.gguf) | i1-IQ2_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ2_M.gguf) | i1-IQ2_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q2_K.gguf) | i1-Q2_K | 1.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ3_S.gguf) | i1-IQ3_S | 1.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ3_M.gguf) | i1-IQ3_M | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q4_0.gguf) | i1-Q4_0 | 2.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q4_1.gguf) | i1-Q4_1 | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/balinese_tts_model-i1-GGUF/resolve/main/balinese_tts_model.i1-Q6_K.gguf) | i1-Q6_K | 2.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
yokatrawer/blockassist-bc-short_robust_bee_1756160103
|
yokatrawer
| 2025-08-25T22:15:46Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"short robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:15:23Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- short robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756160105
|
Dejiat
| 2025-08-25T22:15:29Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:15:26Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Mitchins/deberta-v3-small-literary-explicitness
|
Mitchins
| 2025-08-25T22:13:21Z
| 0
| 0
| null |
[
"safetensors",
"deberta-v2",
"text-classification",
"literary-analysis",
"content-moderation",
"explicitness-detection",
"deberta-v3",
"pytorch",
"focal-loss",
"en",
"base_model:microsoft/deberta-v3-small",
"base_model:finetune:microsoft/deberta-v3-small",
"license:apache-2.0",
"model-index",
"region:us"
] |
text-classification
| 2025-08-25T21:45:58Z
|
---
language:
- en
license: apache-2.0
base_model: microsoft/deberta-v3-small
tags:
- text-classification
- literary-analysis
- content-moderation
- explicitness-detection
- deberta-v3
- pytorch
- focal-loss
pipeline_tag: text-classification
model-index:
- name: deberta-v3-small-explicit-classifier-v2
results:
- task:
type: text-classification
name: Literary Explicitness Classification
dataset:
name: Custom Literary Dataset (Deduplicated)
type: custom
metrics:
- type: accuracy
value: 0.818
name: Accuracy
- type: f1
value: 0.754
name: Macro F1
- type: f1
value: 0.816
name: Weighted F1
widget:
- text: "Content warning: This story contains mature themes including explicit sexual content and violence."
example_title: "Content Disclaimer"
- text: "His hand lingered on hers as he helped her from the carriage, their fingers intertwining despite propriety."
example_title: "Suggestive Romance"
- text: "She gasped as he traced kisses down her neck, his hands exploring the curves of her body with growing urgency."
example_title: "Explicit Sexual"
- text: "The morning mist drifted across the Yorkshire moors as Elizabeth walked the familiar path to the village."
example_title: "Non-Explicit Literary"
---
# Literary Content Classifier - DeBERTa v3 Small (v2.0)
An improved fine-tuned DeBERTa-v3-small model for sophisticated literary content analysis across 7 categories of explicitness. This v2.0 model features **significant improvements** over the original, including focal loss training, extended epochs, and data quality enhancements.
## 🚀 Key Improvements in v2.0
- **+4.5% accuracy improvement** (81.8% vs 77.3%)
- **+6.4% macro F1 improvement** (0.754 vs 0.709)
- **+21% improvement on violent content** (F1: 0.581 vs 0.478)
- **+19% improvement on suggestive content** (F1: 0.476 vs 0.400)
- **Focal loss training** for better minority class performance
- **Clean dataset** with cross-split contamination resolved
- **Extended training** (4.79 epochs vs 1.1 epochs)
## Model Description
This model provides nuanced classification of textual content across 7 categories, enabling sophisticated analysis for digital humanities, content curation, and literary research applications.
### Categories
| ID | Category | Description | F1 Score |
|----|----------|-------------|----------|
| 0 | EXPLICIT-DISCLAIMER | Content warnings and age restriction notices | **0.977** |
| 1 | EXPLICIT-OFFENSIVE | Profanity, crude language, offensive content | **0.813** |
| 2 | EXPLICIT-SEXUAL | Graphic sexual content and detailed intimate scenes | **0.930** |
| 3 | EXPLICIT-VIOLENT | Violent or disturbing content | **0.581** |
| 4 | NON-EXPLICIT | Clean, family-friendly content | **0.851** |
| 5 | SEXUAL-REFERENCE | Mentions of sexual topics without graphic description | **0.652** |
| 6 | SUGGESTIVE | Mild innuendo or romantic themes without explicit detail | **0.476** |
## Performance Metrics
### Overall Performance
- **Accuracy**: 81.8%
- **Macro F1**: 0.754
- **Weighted F1**: 0.816
### Detailed Results (Test Set)
```
precision recall f1-score support
EXPLICIT-DISCLAIMER 0.95 1.00 0.98 19
EXPLICIT-OFFENSIVE 0.82 0.88 0.81 414
EXPLICIT-SEXUAL 0.93 0.91 0.93 514
EXPLICIT-VIOLENT 0.44 0.62 0.58 24
NON-EXPLICIT 0.77 0.87 0.85 683
SEXUAL-REFERENCE 0.63 0.73 0.65 212
SUGGESTIVE 0.37 0.46 0.48 134
accuracy 0.82 2000
macro avg 0.65 0.78 0.75 2000
weighted avg 0.75 0.82 0.82 2000
```
## Training Details
### Model Architecture
- **Base Model**: microsoft/deberta-v3-small
- **Parameters**: 141.9M (6 layers, 768 hidden, 12 attention heads)
- **Vocabulary**: 128,100 tokens
- **Max Sequence Length**: 512 tokens
### Training Configuration
- **Training Method**: Focal Loss (γ=2.0) for class imbalance
- **Epochs**: 4.79 (early stopped)
- **Learning Rate**: 5e-5 with cosine schedule
- **Batch Size**: 16 (effective 32 with gradient accumulation)
- **Warmup Steps**: 1,000
- **Weight Decay**: 0.01
- **Early Stopping**: Patience 5 on macro F1
### Dataset
- **Total Samples**: 119,023 (after deduplication)
- **Training**: 83,316 samples
- **Validation**: 17,853 samples
- **Test**: 17,854 samples
- **Data Quality**: Cross-split contamination eliminated (2,127 duplicates removed)
### Training Environment
- **Framework**: PyTorch + Transformers
- **Hardware**: Apple Silicon (MPS)
- **Training Time**: ~13.7 hours
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
# Load model and tokenizer
model_id = "your-username/deberta-v3-small-explicit-classifier-v2"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Create classification pipeline
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
return_all_scores=True,
truncation=True
)
# Single classification
text = "His hand lingered on hers as he helped her from the carriage."
result = classifier(text)
print(f"Top prediction: {result[0]['label']} ({result[0]['score']:.3f})")
# All class probabilities
for class_result in result:
print(f"{class_result['label']}: {class_result['score']:.3f}")
```
### Recommended Thresholds (F1-Optimized)
For applications requiring specific precision/recall trade-offs:
| Class | Optimal Threshold | Precision | Recall | F1 |
|-------|------------------|-----------|--------|-----|
| EXPLICIT-DISCLAIMER | 0.995 | 0.950 | 1.000 | 0.974 |
| EXPLICIT-OFFENSIVE | 0.626 | 0.819 | 0.829 | 0.824 |
| EXPLICIT-SEXUAL | 0.456 | 0.927 | 0.911 | 0.919 |
| EXPLICIT-VIOLENT | 0.105 | 0.441 | 0.625 | 0.517 |
| NON-EXPLICIT | 0.103 | 0.768 | 0.874 | 0.818 |
| SEXUAL-REFERENCE | 0.355 | 0.629 | 0.726 | 0.674 |
| SUGGESTIVE | 0.530 | 0.370 | 0.455 | 0.408 |
## Model Files
- `model.safetensors`: Model weights in SafeTensors format
- `config.json`: Model configuration with proper label mappings
- `tokenizer.json`, `spm.model`: SentencePiece tokenizer files
- `label_mapping.json`: Label ID to name mapping reference
## Limitations & Considerations
1. **Challenging Distinctions**: SUGGESTIVE vs SEXUAL-REFERENCE categories remain difficult to distinguish due to conceptual overlap
2. **Minority Classes**: EXPLICIT-VIOLENT and SUGGESTIVE classes have lower F1 scores due to limited training data
3. **Context Dependency**: Short text snippets may lack sufficient context for accurate classification
4. **Domain Specificity**: Optimized for literary and review content; performance may vary on other text types
5. **Language**: English text only
## Evaluation Artifacts
The model includes comprehensive evaluation materials:
- Confusion matrix visualization
- Per-class precision-recall curves
- ROC curves for all categories
- Calibration analysis
- Recommended decision thresholds
## Ethical Use
This model is designed for:
- Academic research and digital humanities
- Content curation and library science applications
- Literary analysis and publishing workflows
- Educational content assessment
**Important**: This model should be used responsibly with human oversight for content moderation decisions.
## Citation
```bibtex
@misc{literary-explicit-classifier-v2-2025,
title={Literary Content Analysis: Improved Multi-Class Classification with Focal Loss},
author={Explicit Content Research Team},
year={2025},
note={DeBERTa-v3-small fine-tuned for literary explicitness detection}
}
```
## License
This model is released under the Apache 2.0 license.
|
poffusers/3b28bdbf
|
poffusers
| 2025-08-25T22:12:16Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"region:us"
] | null | 2025-08-25T22:05:41Z
|
---
title: Test Hugsim Web Server
emoji: 📈
colorFrom: purple
colorTo: yellow
sdk: docker
pinned: false
---
|
yokatrawer/blockassist-bc-short_robust_bee_1756159888
|
yokatrawer
| 2025-08-25T22:12:14Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"short robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:11:48Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- short robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1756158401
|
fujiantiiazhraa
| 2025-08-25T22:11:00Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:10:55Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
znaal/blockassist-bc-tropical_grunting_salamander_1756159748
|
znaal
| 2025-08-25T22:10:57Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tropical grunting salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:10:47Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tropical grunting salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756158139
|
chainway9
| 2025-08-25T22:10:13Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:10:09Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756159698
|
Dejiat
| 2025-08-25T22:08:42Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:08:39Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yokatrawer/blockassist-bc-short_robust_bee_1756159675
|
yokatrawer
| 2025-08-25T22:08:34Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"short robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:08:16Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- short robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1756157878
|
katanyasekolah
| 2025-08-25T22:06:35Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:06:31Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756159557
|
Dejiat
| 2025-08-25T22:06:27Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:06:21Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
g-assismoraes/Qwen3-4B-Base-tile-base-alpha0.8-var-hatebr
|
g-assismoraes
| 2025-08-25T22:06:02Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T21:55:27Z
|
---
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]
|
yokatrawer/blockassist-bc-short_robust_bee_1756159438
|
yokatrawer
| 2025-08-25T22:05:03Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"short robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:04:24Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- short robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756159411
|
Dejiat
| 2025-08-25T22:03:58Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:03:55Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hogensynoo/blockassist-bc-tropical_short_reindeer_1756159339
|
hogensynoo
| 2025-08-25T22:02:49Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tropical short reindeer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:02:21Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tropical short reindeer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rypsor/biomedical-classifier
|
Rypsor
| 2025-08-25T22:02:28Z
| 0
| 0
| null |
[
"safetensors",
"bert",
"medical",
"text-classification",
"en",
"base_model:allenai/scibert_scivocab_uncased",
"base_model:finetune:allenai/scibert_scivocab_uncased",
"license:mit",
"region:us"
] |
text-classification
| 2025-08-25T21:13:07Z
|
---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- allenai/scibert_scivocab_uncased
pipeline_tag: text-classification
tags:
- medical
---
|
Guilherme34/Psychologist-Romanian-hf
|
Guilherme34
| 2025-08-25T22:02:24Z
| 0
| 1
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T22:00:13Z
|
---
base_model: meta-llama/Llama-3.2-3B-Instruct
language:
- en
library_name: transformers
license: llama3.2
tags:
- llama-3
- llama
- meta
- facebook
- unsloth
- transformers
---
BETA MODEL, ITS NOT FINISHED
DOES NOT NEED ANY SYSTEM PROMPT, you can leave empty
|
yokatrawer/blockassist-bc-short_robust_bee_1756159237
|
yokatrawer
| 2025-08-25T22:01:24Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"short robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:00:57Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- short robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756159229
|
Dejiat
| 2025-08-25T22:00:59Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:00:53Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MrAzoooz/MrAzoooz
|
MrAzoooz
| 2025-08-25T22:00:52Z
| 0
| 0
|
adapter-transformers
|
[
"adapter-transformers",
"chemistry",
"ar",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"base_model:openai/gpt-oss-120b",
"base_model:adapter:openai/gpt-oss-120b",
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-25T21:55:23Z
|
---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
language:
- ar
- en
metrics:
- accuracy
base_model:
- openai/gpt-oss-120b
new_version: openai/gpt-oss-120b
library_name: adapter-transformers
tags:
- chemistry
---
|
Muapi/painterly-ce-xl-flux
|
Muapi
| 2025-08-25T22:00:38Z
| 0
| 0
| null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T22:00:21Z
|
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Painterly - CE - XL & Flux
**Base model**: Flux.1 D
**Trained words**: pntrlyCE_style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:957327@1071897", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756157498
|
indoempatnol
| 2025-08-25T22:00:23Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T22:00:20Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fera1112/blockassist-bc-secretive_snappy_macaque_1756157789
|
fera1112
| 2025-08-25T21:59:50Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"secretive snappy macaque",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T21:59:42Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- secretive snappy macaque
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/majicflus-horror
|
Muapi
| 2025-08-25T21:59:45Z
| 0
| 0
| null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T21:59:21Z
|
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# MajicFlus horror 麦橘恐怖
**Base model**: Flux.1 D
**Trained words**: horror
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1115551@1253584", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
AnonymousCS/populism_classifier_052
|
AnonymousCS
| 2025-08-25T21:59:38Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T21:58:26Z
|
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_052
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_052
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4192
- Accuracy: 0.8966
- 1-f1: 0.4742
- 1-recall: 0.7419
- 1-precision: 0.3485
- Balanced Acc: 0.8244
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1249 | 1.0 | 31 | 0.4141 | 0.9391 | 0.5946 | 0.7097 | 0.5116 | 0.8321 |
| 0.1033 | 2.0 | 62 | 0.4809 | 0.9371 | 0.5634 | 0.6452 | 0.5 | 0.8009 |
| 0.1211 | 3.0 | 93 | 0.4192 | 0.8966 | 0.4742 | 0.7419 | 0.3485 | 0.8244 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1756157611
|
vwzyrraz7l
| 2025-08-25T21:59:16Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T21:59:12Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/star-wars-clone-wars-armour-phase-2-flux-sdxl-pony
|
Muapi
| 2025-08-25T21:59:06Z
| 0
| 0
| null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T21:58:56Z
|
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Star Wars: Clone Wars Armour Phase 2 FLUX/SDXL/PONY
**Base model**: Flux.1 D
**Trained words**: 7-Phase2, Clone Trooper
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:546650@785374", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
AnonymousCS/populism_classifier_051
|
AnonymousCS
| 2025-08-25T21:58:23Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T21:57:01Z
|
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_051
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_051
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5789
- Accuracy: 0.9640
- 1-f1: 0.6129
- 1-recall: 0.5429
- 1-precision: 0.7037
- Balanced Acc: 0.7651
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2982 | 1.0 | 42 | 0.3183 | 0.9595 | 0.64 | 0.6857 | 0.6 | 0.8302 |
| 0.2002 | 2.0 | 84 | 0.3302 | 0.9595 | 0.6197 | 0.6286 | 0.6111 | 0.8032 |
| 0.1408 | 3.0 | 126 | 0.5789 | 0.9640 | 0.6129 | 0.5429 | 0.7037 | 0.7651 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Muapi/aitk-cineflux
|
Muapi
| 2025-08-25T21:58:21Z
| 0
| 0
| null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T21:57:50Z
|
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# AITK-CineFlux
**Base model**: Flux.1 D
**Trained words**: cinematic, film still, screengrab
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:677864@826854", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/joschek-s-latex-bodysuits-for-flux
|
Muapi
| 2025-08-25T21:57:24Z
| 0
| 0
| null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T21:57:00Z
|
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Joschek's Latex Bodysuits for Flux
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:853926@955369", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/virtual-asian-female-character
|
Muapi
| 2025-08-25T21:56:42Z
| 0
| 0
| null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T21:55:47Z
|
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Virtual Asian Female Character
**Base model**: Flux.1 D
**Trained words**: llz
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:754249@1904086", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
hogensynoo/blockassist-bc-invisible_scented_dinosaur_1756158955
|
hogensynoo
| 2025-08-25T21:56:31Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible scented dinosaur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T21:55:57Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible scented dinosaur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756158950
|
Dejiat
| 2025-08-25T21:56:20Z
| 0
| 0
| null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T21:56:15Z
|
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
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