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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"execution_state": "idle",
"id": "1ddfc692-bda7-4d38-a549-2fb0d40d437d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at /home/sipb/nlp-class-project/checkpoints/bert_base_rtl/epoch_3_checkpt were not used when initializing BertForMaskedLM: ['attention_mask']\n",
"- This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM 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 BertForMaskedLM were not initialized from the model checkpoint at /home/sipb/nlp-class-project/checkpoints/bert_base_rtl/epoch_3_checkpt and are newly initialized because the shapes did not match:\n",
"- bert.embeddings.position_embeddings.weight: found shape torch.Size([512, 768]) in the checkpoint and torch.Size([128, 768]) in the model instantiated\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"import math\n",
"import sys\n",
"\n",
"sys.path.append(\"..\")\n",
"\n",
"import torch\n",
"import transformers\n",
"\n",
"from utils import add_attn_hooks\n",
"\n",
"# tokenizer = transformers.AutoTokenizer.from_pretrained(\"distilbert/distilbert-base-uncased\")\n",
"# model = transformers.AutoModelForMaskedLM.from_pretrained(\"/home/sipb/nlp-class-project/checkpoints/distilbert_base_ltr/epoch_3_checkpt\", ignore_mismatched_sizes=True)\n",
"\n",
"tokenizer = transformers.AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
"text_dir = \"rtl\"\n",
"# text_dir = \"ltr\"\n",
"model = transformers.AutoModelForMaskedLM.from_pretrained(f\"/home/sipb/nlp-class-project/checkpoints/bert_base_{text_dir}/epoch_3_checkpt\", ignore_mismatched_sizes=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"execution_state": "idle",
"id": "a732375b-1682-45c6-8df0-8db1458559c9",
"metadata": {},
"outputs": [],
"source": [
"add_attn_hooks(model, text_dir)\n",
"model.eval();"
]
},
{
"cell_type": "code",
"execution_count": 16,
"execution_state": "idle",
"id": "041d1702-5aaf-45f0-9413-4014b315d1ed",
"metadata": {},
"outputs": [],
"source": [
"with open(\"/home/sipb/nlp-class-project/data/riddles.txt\", \"r\") as f:\n",
" riddles = [line.rstrip() for line in f.readlines()]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"execution_state": "idle",
"id": "a4098975-2df6-4435-bc93-1a5afd6d7e68",
"metadata": {},
"outputs": [],
"source": [
"# CPU is fast enough\n",
"\n",
"ppls = []\n",
"for riddle in riddles:\n",
" batch = tokenizer([riddle], return_tensors=\"pt\", padding_side=\"left\" if text_dir == \"rtl\" else \"right\", padding=\"max_length\", max_length=128)\n",
" batch[\"labels\"] = batch[\"input_ids\"].clone()\n",
" batch[\"labels\"][batch[\"attention_mask\"] == 0] = -100\n",
" # batch = tokenizer([riddle], return_tensors=\"pt\")#, padding_side=\"left\" if text_dir == \"rtl\" else \"right\", padding=\"longest\", max_length=128)\n",
" # batch[\"labels\"] = batch[\"input_ids\"]\n",
" with torch.inference_mode():\n",
" output = model(**batch)\n",
" ppls.append(math.e ** output.loss.item())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_state": "idle",
"id": "c4a82af4-d0d8-415a-9135-3a1350c1402e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(522.113471240328, 'rtl')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(ppls) / len(ppls), text_dir"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_state": "idle",
"id": "84a95c66-6dd3-4ccb-96a2-96f38008f70e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1007.5656859988405, 'ltr')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(ppls) / len(ppls), text_dir"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_state": "idle",
"id": "51ed80f1-a935-42bc-8194-832f91222c45",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1007.5656309474507, 'ltr')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(ppls) / len(ppls), text_dir"
]
},
{
"cell_type": "code",
"execution_count": 21,
"execution_state": "idle",
"id": "40a98c10-59c3-498a-a9e6-c23bd9437bc7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"937.8557468023619"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(ppls) / len(ppls)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"execution_state": "idle",
"id": "80b22ba1-e5ba-4f1e-8038-158a2c2f37a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input_ids': tensor([[ 101, 1045, 2064, 2022, 2524, 1010, 2021, 1045, 2572, 2025,\n",
" 5024, 1012, 2054, 2572, 1045, 1029, 1037, 15117, 1012, 102,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0]]), 'labels': tensor([[ 101, 1045, 2064, 2022, 2524, 1010, 2021, 1045, 2572, 2025,\n",
" 5024, 1012, 2054, 2572, 1045, 1029, 1037, 15117, 1012, 102,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
" -100, -100, -100, -100, -100, -100, -100, -100]])}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch"
]
},
{
"cell_type": "code",
"execution_count": 16,
"execution_state": "idle",
"id": "c68b5235-a4a7-4f38-9acb-f5072e546a96",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 4., 6., 11., 6., 5., 2., 1., 1., 2., 2.]),\n",
" array([ 613.56297843, 829.36555779, 1045.16813716, 1260.97071653,\n",
" 1476.77329589, 1692.57587526, 1908.37845463, 2124.18103399,\n",
" 2339.98361336, 2555.78619272, 2771.58877209]),\n",
" <BarContainer object of 10 artists>)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.hist(ppls)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_state": "idle",
"id": "8acad3ce-905d-455e-af5d-9770495f374a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414,\n",
" 956.7294281325414]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ppls"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86daa05b-5784-457b-b65e-8b8395128d6f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
