add fairseq_introduction.ipynb
This commit is contained in:
Jakub Pokrywka
parent
0f409ae23b
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b92722a314
722
fairseq_introduction.ipynb
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722
fairseq_introduction.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://github.com/pytorch/fairseq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://fairseq.readthedocs.io/en/latest/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: fairseq in /home/kuba/Syncthing/przedmioty/tau/fairseq (1.0.0a0+1164a7f)\n",
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"Requirement already satisfied: cffi in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (1.14.0)\n",
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"Requirement already satisfied: cython in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (0.29.21)\n",
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"Requirement already satisfied: hydra-core<1.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (1.0.5)\n",
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"Requirement already satisfied: omegaconf<2.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (2.0.5)\n",
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"Requirement already satisfied: regex in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (2020.11.13)\n",
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"Requirement already satisfied: sacrebleu>=1.4.12 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (1.4.14)\n",
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"Requirement already satisfied: torch in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (1.7.0)\n",
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"Requirement already satisfied: tqdm in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (4.54.1)\n",
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"Requirement already satisfied: numpy in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from fairseq) (1.19.2)\n",
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"Requirement already satisfied: pycparser in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from cffi->fairseq) (2.20)\n",
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"Requirement already satisfied: importlib-resources; python_version < \"3.9\" in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from hydra-core<1.1->fairseq) (5.0.0)\n",
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"Requirement already satisfied: antlr4-python3-runtime==4.8 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from hydra-core<1.1->fairseq) (4.8)\n",
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"Requirement already satisfied: PyYAML>=5.1.* in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from omegaconf<2.1->fairseq) (5.4b2)\n",
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"Requirement already satisfied: typing-extensions in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from omegaconf<2.1->fairseq) (3.7.4.3)\n",
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"Requirement already satisfied: portalocker in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from sacrebleu>=1.4.12->fairseq) (2.0.0)\n",
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"Requirement already satisfied: future in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from torch->fairseq) (0.18.2)\n",
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"Requirement already satisfied: dataclasses in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from torch->fairseq) (0.6)\n"
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]
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}
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],
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"source": [
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"!pip install fairseq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: fastBPE in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (0.1.0)\n",
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"Requirement already satisfied: regex in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (2020.11.13)\n",
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"Requirement already satisfied: requests in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (2.25.0)\n",
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"Requirement already satisfied: sacremoses in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (0.0.43)\n",
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"Requirement already satisfied: subword_nmt in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (0.3.7)\n",
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"Requirement already satisfied: chardet<4,>=3.0.2 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests) (3.0.4)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests) (2020.12.5)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests) (1.25.11)\n",
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"Requirement already satisfied: idna<3,>=2.5 in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from requests) (2.10)\n",
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"Requirement already satisfied: tqdm in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from sacremoses) (4.54.1)\n",
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"Requirement already satisfied: click in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from sacremoses) (7.1.2)\n",
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"Requirement already satisfied: six in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from sacremoses) (1.15.0)\n",
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"Requirement already satisfied: joblib in /home/kuba/anaconda3/envs/tau/lib/python3.8/site-packages (from sacremoses) (0.17.0)\n"
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]
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}
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],
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"source": [
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"!pip install fastBPE regex requests sacremoses subword_nmt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch, fairseq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://github.com/pytorch/fairseq/blob/master/examples/language_model/README.md"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using cache found in /home/kuba/.cache/torch/hub/pytorch_fairseq_master\n"
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]
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}
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],
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"source": [
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"en_lm = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"#en_lm.cuda()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Barack Obama'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"en_lm.sample('Barack Obama', beam=1, sampling=True, sampling_topk=10, temperature=0.8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"del en_lm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://github.com/pytorch/fairseq/tree/master/examples/roberta"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using cache found in /home/kuba/.cache/torch/hub/pytorch_fairseq_master\n"
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]
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}
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],
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"source": [
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"roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# roberta.cuda()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"RobertaHubInterface(\n",
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" (model): RobertaModel(\n",
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" (encoder): RobertaEncoder(\n",
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" (sentence_encoder): TransformerSentenceEncoder(\n",
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" (dropout_module): FairseqDropout()\n",
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" (embed_tokens): Embedding(50265, 768, padding_idx=1)\n",
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" (embed_positions): LearnedPositionalEmbedding(514, 768, padding_idx=1)\n",
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" (emb_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (layers): ModuleList(\n",
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" (0): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (1): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (2): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (3): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (4): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (5): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (6): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (7): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
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" (activation_dropout_module): FairseqDropout()\n",
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" (self_attn): MultiheadAttention(\n",
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" (dropout_module): FairseqDropout()\n",
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" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
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" )\n",
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" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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" )\n",
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" (8): TransformerSentenceEncoderLayer(\n",
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" (dropout_module): FairseqDropout()\n",
|
||||
" (activation_dropout_module): FairseqDropout()\n",
|
||||
" (self_attn): MultiheadAttention(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" )\n",
|
||||
" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
||||
" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
||||
" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" )\n",
|
||||
" (9): TransformerSentenceEncoderLayer(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (activation_dropout_module): FairseqDropout()\n",
|
||||
" (self_attn): MultiheadAttention(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" )\n",
|
||||
" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
||||
" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
||||
" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" )\n",
|
||||
" (10): TransformerSentenceEncoderLayer(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (activation_dropout_module): FairseqDropout()\n",
|
||||
" (self_attn): MultiheadAttention(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" )\n",
|
||||
" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
||||
" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
||||
" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" )\n",
|
||||
" (11): TransformerSentenceEncoderLayer(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (activation_dropout_module): FairseqDropout()\n",
|
||||
" (self_attn): MultiheadAttention(\n",
|
||||
" (dropout_module): FairseqDropout()\n",
|
||||
" (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" )\n",
|
||||
" (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
||||
" (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
||||
" (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (lm_head): RobertaLMHead(\n",
|
||||
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
||||
" (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (classification_heads): ModuleDict()\n",
|
||||
" )\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"roberta.eval()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tokens = roberta.encode('Hello world!')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"last_layer_features = roberta.extract_features(tokens)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_layers = roberta.extract_features(tokens, return_all_hiddens=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"assert torch.all(all_layers[-1] == last_layer_features)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using cache found in /home/kuba/.cache/torch/hub/pytorch_fairseq_master\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Encode a pair of sentences and make a prediction\n",
|
||||
"tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"roberta.predict('mnli', tokens).argmax() # 0: contradiction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Encode another pair of sentences\n",
|
||||
"tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"roberta.predict('mnli', tokens).argmax() # 2: entailment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# contradiction - sprzeczność\n",
|
||||
"# neutral\n",
|
||||
"# entailment - wynikanie\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"roberta.fill_mask('<mask> is the common currency of the European Union', topk=3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"del roberta"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"###### FAIRSEQ TRANSLATION https://pytorch.org/hub/pytorch_fairseq_translation/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de',\n",
|
||||
" tokenizer='moses', bpe='subword_nmt')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en2de.eval()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# en2de.cuda()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en2de.translate('Hello world!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en2de.translate(['Hello world!', 'The cat sat on the mat.'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en2de.translate(['Hello world!', 'The cat sat on the mat.'], beam=5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en_toks = en2de.tokenize('Hello world!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en_bpe = en2de.apply_bpe(en_toks)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"en_bin = en2de.binarize(en_bpe)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"de_bin = en2de.generate(en_bin, beam=5, sampling=True, sampling_topk=20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"de_sample = de_bin[0]['tokens']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"de_bpe = en2de.string(de_sample)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"de_toks = en2de.remove_bpe(de_bpe)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# trenowanie https://github.com/pytorch/fairseq/blob/master/examples/translation/README.md"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"del en2de"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user