ArTST: Arabic Text and Speech Transformer
Paper
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2310.16621
•
Published
•
2
AI & ML interests
None defined yet.
herwoww
authored
6
papers
8 months ago
Exploring the Limitations of Detecting Machine-Generated Text
Paper
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2406.11073
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Published
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Paper
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2411.16508
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Published
•
10
Dialectal Coverage And Generalization in Arabic Speech Recognition
Paper
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2411.05872
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Published
•
2
ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis
Paper
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2505.20506
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Published
Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study
Paper
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2506.07722
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Published
clefourrier
posted
an
update
9 months ago
Post
2283
Always surprised that so few people actually read the FineTasks blog, on
✨how to select training evals with the highest signal✨
If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!
An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!
The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)
Blog: HuggingFaceFW/blogpost-fine-tasks
✨how to select training evals with the highest signal✨
If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!
An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!
The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)
Blog: HuggingFaceFW/blogpost-fine-tasks
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- 2 replies
clefourrier
posted
an
update
11 months ago
Post
2686
Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
elmadany
published
a
Space
12 months ago
elmadany
updated
a
Space
12 months ago
clefourrier
authored
a
paper
12 months ago
clefourrier
authored
a
paper
about 1 year ago
mageed
authored
a
paper
about 1 year ago
mageed
authored
a
paper
over 1 year ago
clefourrier
authored
2
papers
over 1 year ago
clefourrier
posted
an
update
almost 2 years ago
Post
6176
In a basic chatbots, errors are annoyances. In medical LLMs, errors can have life-threatening consequences 🩸
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co/blog/leaderboard-medicalllm
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co/blog/leaderboard-medicalllm
clefourrier
posted
an
update
almost 2 years ago
Post
4798
Contamination free code evaluations with LiveCodeBench! 🖥️
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co/blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co/blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
clefourrier
posted
an
update
almost 2 years ago
Post
2280
🆕 Evaluate your RL agents - who's best at Atari?🏆
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
clefourrier
posted
an
update
almost 2 years ago
Post
2266
Fun fact about evaluation, part 2!
How much do scores change depending on prompt format choice?
Using different prompts (all present in the literature, from
10 points for a single model!
Keep in mind that we only changed the prompt, not the evaluation subsets, etc.
Again, this confirms that evaluation results reported without their details are basically bullshit.
Prompt format on the x axis, all these evals look at the logprob of either "choice A/choice B..." or "A/B...".
Incidentally, it also changes model rankings - so a "best" model might only be best on one type of prompt...
How much do scores change depending on prompt format choice?
Using different prompts (all present in the literature, from
Prompt question? to Question: prompt question?\nChoices: enumeration of all choices\nAnswer: ), we get a score range of...10 points for a single model!
Keep in mind that we only changed the prompt, not the evaluation subsets, etc.
Again, this confirms that evaluation results reported without their details are basically bullshit.
Prompt format on the x axis, all these evals look at the logprob of either "choice A/choice B..." or "A/B...".
Incidentally, it also changes model rankings - so a "best" model might only be best on one type of prompt...