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Diffstat (limited to 'notebooks/Riddles.ipynb')
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1 files changed, 362 insertions, 0 deletions
diff --git a/notebooks/Riddles.ipynb b/notebooks/Riddles.ipynb new file mode 100644 index 0000000..c2c8309 --- /dev/null +++ b/notebooks/Riddles.ipynb @@ -0,0 +1,362 @@ +{ + "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, 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}, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} |