Souvika Sarkar, Mousumi Akter, Shubhra Kanti, " Santu
We lifted 11 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/fastText | canonical | 7 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| find_nearest_neighbor | Ran | facebookresearch/fastText/python/fasttext_module/fasttext/util/util.py pointer only (licence: MIT) · get_code("5d48a5764718f390") |
| guess_target_name | Ran | facebookresearch/fastText/reduce_model.py pointer only (licence: MIT) · get_code("c2a80677a6cb6044") |
| idx | Ran | facebookresearch/fastText/alignment/utils.py pointer only (licence: MIT) · get_code("831f6e244ecea2bb") |
| procrustes | Ran | facebookresearch/fastText/alignment/utils.py pointer only (licence: MIT) · get_code("6f28ff1eccefb093") |
| read_args | Ran | facebookresearch/fastText/python/fasttext_module/fasttext/FastText.py pointer only (licence: MIT) · get_code("f697966cbe929024") |
| sqrt_eig | Ran | facebookresearch/fastText/alignment/unsup_align.py pointer only (licence: MIT) · get_code("e4a10d52bc6d1601") |
| test | Ran | facebookresearch/fastText/python/fasttext_module/fasttext/util/util.py pointer only (licence: MIT) · get_code("5c3bcf5ae2042c99") |
| getknn | Not yet run | facebookresearch/fastText/alignment/align.py pointer only (licence: MIT) · get_code("f685d8c430dfac70") |
| load_vectors | Not yet run | facebookresearch/fastText/alignment/utils.py pointer only (licence: MIT) · get_code("5b38e4065c54800a") |
| rcsls | Not yet run | facebookresearch/fastText/alignment/unsup_multialign.py pointer only (licence: MIT) · get_code("d484b37352fc8959") |
| reduce_model | Not yet run | facebookresearch/fastText/python/fasttext_module/fasttext/util/util.py pointer only (licence: MIT) · get_code("3ab9d330ea24c434") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings. Despite being the 7 th largest spoken language in the world, Bangla is still a low-resource language and popular NLP models often struggle to perform well on Bangla data sets. Therefore, developing a robust evaluation set is crucial for benchmarking and guiding future research on improving Bangla word embeddings, which is currently missing. To address this issue, we introduce a new evaluation set of 16,678 unique word analogies in Bangla as well as a translated and curated version of the original Mikolov dataset (10,594 samples) in Bangla. Our experiments with different state-of-the-art embedding models reveal that current Bangla word embeddings struggle to achieve high accuracy on both data sets, demonstrating a significant gap in multilingual NLP research.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2304.04613")
get_code_for_paper("2304.04613")
have("2304.04613")
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