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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\FYP\\lilt-app-without-fd\\lilt-env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from transformers import LiltModel, AutoTokenizer, LiltForTokenClassification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('models/lilt-tokenizer\\\\tokenizer_config.json',\n",
" 'models/lilt-tokenizer\\\\special_tokens_map.json',\n",
" 'models/lilt-tokenizer\\\\tokenizer.json')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TOKENIZER = 'nielsr/lilt-xlm-roberta-base'\n",
"tokenizer = AutoTokenizer.from_pretrained(TOKENIZER)\n",
"save_dir = 'models/lilt-tokenizer'\n",
"tokenizer.save_pretrained(save_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download and save token classification model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# download the model\n",
"MODEL = \"pierreguillou/lilt-xlm-roberta-base-finetuned-funsd-iob-original\"\n",
"model = LiltForTokenClassification.from_pretrained(MODEL)\n",
"\n",
"# save the model\n",
"save_dir = \"models/lilt-ser-iob\"\n",
"model.save_pretrained(save_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download and save RE model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading config.json: 100%|██████████| 794/794 [00:00<00:00, 61.2kB/s]\n",
"d:\\FYP\\lilt-app-without-fd\\lilt-env\\lib\\site-packages\\huggingface_hub\\file_download.py:133: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Gihantha Kavishka\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
"To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
" warnings.warn(message)\n",
"Downloading pytorch_model.bin: 100%|██████████| 1.15G/1.15G [08:10<00:00, 2.34MB/s]\n",
"Some weights of the model checkpoint at kavg/layoutxlm-finetuned-xfund-fr-re were not used when initializing LiltModel: ['extractor.rel_classifier.linear.weight', 'extractor.entity_emb.weight', 'extractor.ffnn_tail.0.weight', 'extractor.ffnn_tail.3.bias', 'extractor.ffnn_head.3.weight', 'extractor.ffnn_head.0.weight', 'extractor.ffnn_tail.0.bias', 'extractor.ffnn_head.3.bias', 'extractor.rel_classifier.bilinear.weight', 'extractor.rel_classifier.linear.bias', 'extractor.ffnn_head.0.bias', 'extractor.ffnn_tail.3.weight']\n",
"- This IS expected if you are initializing LiltModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing LiltModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of LiltModel were not initialized from the model checkpoint at kavg/layoutxlm-finetuned-xfund-fr-re and are newly initialized: ['lilt.pooler.dense.bias', 'lilt.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"# download the model\n",
"MODEL = 'kavg/layoutxlm-finetuned-xfund-fr-re'\n",
"model = LiltModel.from_pretrained(MODEL)\n",
"\n",
"# save the model\n",
"save_dir = \"models/lilt-re\"\n",
"model.save_pretrained(save_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "lilt-env",
"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": "3.7.8"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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