datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FineFineWeb-fasttext-seeddata
FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus
arXiv: Coming Soon
Project Page: Coming Soon
Blog: Coming Soon
Data Statistics
Domain (#tokens/#samples)
Iteration 1 Tokens
Iteration 2 Tokens
Iteration 3 Tokens
Total Tokens
Iteration 1 Count
Iteration 2 Count
Iteration 3 Count
Total Count
aerospace
5.77B
261.63M
309.33M
6.34B
9100000
688505
611034
10399539
agronomy
13.08B
947.41M
229.04M
14.26B
15752828
2711790
649404
19114022
artistic… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/FineFineWeb-fasttext-seeddata.fasttext-datab2_code_fasttext_pos_ioi_neg_sql_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_ioi_neg_sql_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
16.7
57.8
76.2
27.8
39.4
43.4
41.9
14.9
17.7
AIME24
Average Accuracy: 16.67% ± 1.33%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
10.00%
3
30
2
13.33%
4
30
3
16.67%
5
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_ioi_neg_sql_eval_636d.e1_code_fasttext_phi_temp2coconot_fasttext_filter_25B_tokensdclm-baseline-fasttextb2_math_fasttext_pos_openr1_math_neg_lap1official_math_eval_636d
mlfoundations-dev/b2_math_fasttext_pos_openr1_math_neg_lap1official_math_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
19.7
65.2
80.4
28.2
38.4
44.9
15.1
4.9
6.2
AIME24
Average Accuracy: 19.67% ± 1.97%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
16.67%
5
30
2
16.67%
5… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_math_fasttext_pos_openr1_math_neg_lap1official_math_eval_636d.e1_code_fasttext_r1_eval_636d
mlfoundations-dev/e1_code_fasttext_r1_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
21.7
65.5
75.8
0.4
42.7
40.9
22.7
14.9
20.0
AIME24
Average Accuracy: 21.67% ± 2.07%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
13.33%
4
30
2
30.00%
9
30
3
16.67%
5
30
4
26.67%
8… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/e1_code_fasttext_r1_eval_636d.fineweb-edu-v2-fasttextprocessed_arabic_embeddings_fasttext_ar_vectorsb2_code_fasttext_pos_codeforces_neg_all_1k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_all_1k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
16.0
55.0
71.6
26.0
37.7
36.4
29.5
7.2
8.8
AIME24
Average Accuracy: 16.00% ± 1.32%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
16.67%
5
30
2
16.67%
5
30
3… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_all_1k_eval_636d.b2_code_fasttext_pos_codeforces_neg_codereviewmc4-und-fasttextinstruction_filtering_scale_up_code_base_fasttext_per_domaingru_fasttext_model
Gojek Statement Review Classifier
This application is designed to classify review statements into positive, neutral, or negative sentiments using traditional machine learning and deep learning models, built on Gojek review data
See more on web demo, and github:
[1] https://gojek-sentiment-review-classifier-kelompok6.streamlit.app/
[2] https://github.com/FebryantoAdityaRizky020204/gojek-sentiment-review-classifier/tree/main
distemist-fasttext-8-nerhttps://temu.bsc.es/multicardioner/b2_code_fasttext_pos_all_neg_sql_10k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_all_neg_sql_10k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
19.7
60.8
73.8
25.2
41.3
39.1
36.1
12.2
14.1
AIME24
Average Accuracy: 19.67% ± 1.10%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
23.33%
7
30
2
16.67%
5
30
3
23.33%
7… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_all_neg_sql_10k_eval_636d.fasttext-lmdbe1_code_fasttext_r1drugtemist-en-fasttext-85-nerhttps://temu.bsc.es/multicardioner/e1_code_fasttext_r1_10korganic_chemistry_train_fasttextcantemist-fasttext-8-nerhttps://temu.bsc.es/cantemist/b2_math_fasttext_pos_numina_neg_natural_reasoningsymptemist-fasttext-8-nerhttps://temu.bsc.es/symptemist/distemist-fasttext-9-nerhttps://temu.bsc.es/multicardioner/wi_reasoning_fasttext_trainingb2_math_fasttext_pos_brando_olympiad_neg_lap1official_mathe1_code_fasttext_qwqdistemist-fasttext-85-nerhttps://temu.bsc.es/multicardioner/