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0.0.9 plamtral-0.0.9-py3-none-any.whl

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Project: plamtral
Version: 0.0.9
Filename: plamtral-0.0.9-py3-none-any.whl
Download: [link]
Size: 23776
MD5: f73b100da1d888044b1cd9621dcacea9
SHA256: 2f7190ec54a15f004248e1b3af47c06d5484b7d88473bdf71c5c0b2d2b6a6d2d
Uploaded: 2023-03-07 14:20:54 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt

METADATA

Metadata-Version: 2.1
Name: plamtral
Version: 0.0.9
Summary: A transfer learning library for pre-trained transformers.
Author: Vibhu
Author-Email: vibhud04[at]gmail.com
Home-Page: https://github.com/Vibhu04/plamtral
Project-Url: Bug Tracker, https://github.com/Vibhu04/plamtral/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Requires-Dist: nltk
Requires-Dist: torch
Requires-Dist: torchmetrics
Requires-Dist: tqdm
Requires-Dist: transformers
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 3957 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.38.4)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
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data/dataset.py sha256=-qGwE8I7IdwaJIo8KV_r_7N0CLIOsGdt7mMIraKXlCI 1393
fine_tuning/BitFit.py sha256=OH9aSvkWh1L5G2OGv9pcgfOK7mmWwMrGlA4uOBqePVA 732
fine_tuning/ULMFiT.py sha256=AJF2WNHTmZV2h5fR6XCNUicgZRq49svpxLQx_YNr-Ig 1154
fine_tuning/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
fine_tuning/chain_thaw.py sha256=E-On5m3yO79Z6CgFgeEKL-HEWgtyHpAhO1Dklo3Yss4 793
fine_tuning/stlr.py sha256=ch_qH7T9rj-0MAGeZca_Tj8Ls-pvblJkey1qJ8ebmr4 1212
fine_tuning/utils.py sha256=j3nQzpoNQbSaoWPsPDtL2ewrgATz2NP87_cz3Mme3dY 5622
fine_tuning/vanilla_fine_tuning.py sha256=lp8_U2lHuR6vw_gt4_S4zlmVct9f1CwOxe5EpkvQv54 977
parameter_efficient/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
parameter_efficient/adapter.py sha256=eM0LV3_l7SL4TcYwoHDPy-EQpyaG-OWukeIyjWE_-Aw 2342
parameter_efficient/adapter_bapna.py sha256=qi-K0q6u2emUEGoF78fpDZvVd0Ng88SKj8JA0AFNEQg 2073
parameter_efficient/adapterdrop.py sha256=v9UCd51wP2fpbjq54kEovqqSiwyB6xr9CcGF0qxrULc 3372
parameter_efficient/lora.py sha256=ElW7OOskXg3ihBa7KDfjFUzE896uIibGNFIWs367peE 2156
parameter_efficient/mam_adapter.py sha256=EY4Tp8hqHgoEZtG_D64DDz7HGgDOkhmu6N-ZXyEpUc8 2020
parameter_efficient/masking.py sha256=uzdsw16DtMzNZMj42x7v9pOTQrIFXM09pPm1nlhN58M 2613
parameter_efficient/parallel_adapter.py sha256=I3NEx1gggWjXZeQ__dju2kjBDIC-w-KCHJWDuPSru6g 2889
parameter_efficient/prefix_tuning.py sha256=DoUn8v6GPJjtc3_UelRboXnAuA1wDCE1bCZ1L4r1kjU 2917
parameter_efficient/prompt_tuning.py sha256=G5d_pULVbcab4jKrE4kjvmpPwgT0wlvcFGa2HUpIrjI 1600
tl_lib/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
tl_lib/evaluate.py sha256=YAu7QPs_5zYcn-BljOl9Uh-EmOGZ2rCvmpMP6JyoW1A 3242
tl_lib/select_model.py sha256=yOZCUR2P_eyKX6y3icqY0LhmZVfaMR-olI6cjbfn5Ss 1198
tl_lib/tl_train.py sha256=74qMb1dZZz-DQjNTZ_uRFqIhO_B13TyVLa84C1IV7YE 4226
tl_lib/utils.py sha256=zeA7xyhdcOQ1PTtrZ0wpd9V2dHeMo3w8pW2mpoEbinA 5355
plamtral-0.0.9.dist-info/LICENSE sha256=F7ciklDyp3vzYc0YRwbn2BVhdRyHZxtZaytwLdTEz80 1073
plamtral-0.0.9.dist-info/METADATA sha256=BNBQ1IkrIMiSFi-6aLDlVOUJrWSFRkNaHp665Co0DuU 4588
plamtral-0.0.9.dist-info/WHEEL sha256=2wepM1nk4DS4eFpYrW1TTqPcoGNfHhhO_i5m4cOimbo 92
plamtral-0.0.9.dist-info/top_level.txt sha256=weC980JszojvkzWchidSZMOx3M8i_T_wCd8jest2Cc4 44
plamtral-0.0.9.dist-info/RECORD — —

top_level.txt

data
fine_tuning
parameter_efficient
tl_lib