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Browse files- .gitattributes +1 -0
- .hfignore +13 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/config.json +24 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/model.safetensors +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/optimizer.pt +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/rng_state.pth +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/scaler.pt +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/scheduler.pt +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/special_tokens_map.json +7 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/tokenizer.json +0 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/tokenizer_config.json +56 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/trainer_state.json +324 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/training_args.bin +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-4092/vocab.txt +0 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/config.json +24 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/model.safetensors +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/optimizer.pt +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/rng_state.pth +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/scaler.pt +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/scheduler.pt +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/special_tokens_map.json +7 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/tokenizer.json +0 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/tokenizer_config.json +56 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/trainer_state.json +621 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/training_args.bin +3 -0
- Floressek/sentiment_classification_from_distillbert/checkpoint-8184/vocab.txt +0 -0
- Floressek/sentiment_classification_from_distillbert/config.json +24 -0
- Floressek/sentiment_classification_from_distillbert/model.safetensors +3 -0
- Floressek/sentiment_classification_from_distillbert/special_tokens_map.json +7 -0
- Floressek/sentiment_classification_from_distillbert/tokenizer.json +0 -0
- Floressek/sentiment_classification_from_distillbert/tokenizer_config.json +56 -0
- Floressek/sentiment_classification_from_distillbert/training_args.bin +3 -0
- Floressek/sentiment_classification_from_distillbert/vocab.txt +0 -0
- Szymon_Florek_Model_fine_tuning.ipynb +1145 -0
- data/Amazon_Unlocked_Mobile.csv +3 -0
- data/Amazon_Unlocked_Mobile.csv.zip +3 -0
.gitattributes
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Floressek/sentiment_classification_from_distillbert/checkpoint-4092/config.json
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Floressek/sentiment_classification_from_distillbert/checkpoint-4092/tokenizer.json
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Floressek/sentiment_classification_from_distillbert/checkpoint-4092/tokenizer_config.json
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Floressek/sentiment_classification_from_distillbert/checkpoint-4092/trainer_state.json
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/optimizer.pt
ADDED
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/rng_state.pth
ADDED
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/scaler.pt
ADDED
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/scheduler.pt
ADDED
|
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/special_tokens_map.json
ADDED
|
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| 2 |
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| 3 |
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| 7 |
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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Floressek/sentiment_classification_from_distillbert/checkpoint-8184/tokenizer_config.json
ADDED
|
@@ -0,0 +1,56 @@
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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|
| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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|
| 34 |
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| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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| 39 |
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|
| 40 |
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| 41 |
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|
| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 53 |
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| 54 |
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|
| 56 |
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|
Floressek/sentiment_classification_from_distillbert/checkpoint-8184/trainer_state.json
ADDED
|
@@ -0,0 +1,621 @@
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|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"103": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": false,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
+
"do_lower_case": true,
|
| 47 |
+
"extra_special_tokens": {},
|
| 48 |
+
"mask_token": "[MASK]",
|
| 49 |
+
"model_max_length": 512,
|
| 50 |
+
"pad_token": "[PAD]",
|
| 51 |
+
"sep_token": "[SEP]",
|
| 52 |
+
"strip_accents": null,
|
| 53 |
+
"tokenize_chinese_chars": true,
|
| 54 |
+
"tokenizer_class": "DistilBertTokenizer",
|
| 55 |
+
"unk_token": "[UNK]"
|
| 56 |
+
}
|
Floressek/sentiment_classification_from_distillbert/training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0c534883427a071f1d9f3ba8e1a112e5e016307e4c8c2c116719a636f815013c
|
| 3 |
+
size 5841
|
Floressek/sentiment_classification_from_distillbert/vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Szymon_Florek_Model_fine_tuning.ipynb
ADDED
|
@@ -0,0 +1,1145 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"metadata": {
|
| 5 |
+
"collapsed": true
|
| 6 |
+
},
|
| 7 |
+
"cell_type": "markdown",
|
| 8 |
+
"source": "# Fine-tuning a Pretrained Model for sentiment analysis",
|
| 9 |
+
"id": "d26037aadad0840b"
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"metadata": {},
|
| 13 |
+
"cell_type": "markdown",
|
| 14 |
+
"source": "## Importing necessary libraries and data\n",
|
| 15 |
+
"id": "15d09dfe44d655d4"
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"metadata": {
|
| 19 |
+
"ExecuteTime": {
|
| 20 |
+
"end_time": "2025-11-14T17:17:28.523157Z",
|
| 21 |
+
"start_time": "2025-11-14T17:17:23.902525Z"
|
| 22 |
+
}
|
| 23 |
+
},
|
| 24 |
+
"cell_type": "code",
|
| 25 |
+
"source": [
|
| 26 |
+
"!pip install datasets evaluate transformers[sentencepiece]\n",
|
| 27 |
+
"!pip install ipywidgets\n",
|
| 28 |
+
"!pip install torch\n",
|
| 29 |
+
"!pip install transformers[torch]\n",
|
| 30 |
+
"!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n",
|
| 31 |
+
"!pip install scikit-learn"
|
| 32 |
+
],
|
| 33 |
+
"id": "efdad8b2b5ab54d",
|
| 34 |
+
"outputs": [
|
| 35 |
+
{
|
| 36 |
+
"name": "stdout",
|
| 37 |
+
"output_type": "stream",
|
| 38 |
+
"text": [
|
| 39 |
+
"Requirement already satisfied: datasets in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (4.4.1)\n",
|
| 40 |
+
"Requirement already satisfied: evaluate in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (0.4.6)\n",
|
| 41 |
+
"Requirement already satisfied: transformers[sentencepiece] in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (4.57.1)\n",
|
| 42 |
+
"Requirement already satisfied: filelock in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (3.20.0)\n",
|
| 43 |
+
"Requirement already satisfied: numpy>=1.17 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (2.3.4)\n",
|
| 44 |
+
"Requirement already satisfied: pyarrow>=21.0.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (22.0.0)\n",
|
| 45 |
+
"Requirement already satisfied: dill<0.4.1,>=0.3.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (0.4.0)\n",
|
| 46 |
+
"Requirement already satisfied: pandas in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (2.3.3)\n",
|
| 47 |
+
"Requirement already satisfied: requests>=2.32.2 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (2.32.5)\n",
|
| 48 |
+
"Requirement already satisfied: httpx<1.0.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (0.28.1)\n",
|
| 49 |
+
"Requirement already satisfied: tqdm>=4.66.3 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (4.67.1)\n",
|
| 50 |
+
"Requirement already satisfied: xxhash in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (3.6.0)\n",
|
| 51 |
+
"Requirement already satisfied: multiprocess<0.70.19 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (0.70.18)\n",
|
| 52 |
+
"Requirement already satisfied: fsspec<=2025.10.0,>=2023.1.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (2025.10.0)\n",
|
| 53 |
+
"Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (0.36.0)\n",
|
| 54 |
+
"Requirement already satisfied: packaging in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (25.0)\n",
|
| 55 |
+
"Requirement already satisfied: pyyaml>=5.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from datasets) (6.0.3)\n",
|
| 56 |
+
"Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (3.13.2)\n",
|
| 57 |
+
"Requirement already satisfied: anyio in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets) (4.11.0)\n",
|
| 58 |
+
"Requirement already satisfied: certifi in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets) (2025.11.12)\n",
|
| 59 |
+
"Requirement already satisfied: httpcore==1.* in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets) (1.0.9)\n",
|
| 60 |
+
"Requirement already satisfied: idna in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets) (3.11)\n",
|
| 61 |
+
"Requirement already satisfied: h11>=0.16 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from httpcore==1.*->httpx<1.0.0->datasets) (0.16.0)\n",
|
| 62 |
+
"Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (4.15.0)\n",
|
| 63 |
+
"Requirement already satisfied: regex!=2019.12.17 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from transformers[sentencepiece]) (2025.11.3)\n",
|
| 64 |
+
"Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from transformers[sentencepiece]) (0.22.1)\n",
|
| 65 |
+
"Requirement already satisfied: safetensors>=0.4.3 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from transformers[sentencepiece]) (0.6.2)\n",
|
| 66 |
+
"Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from transformers[sentencepiece]) (0.2.1)\n",
|
| 67 |
+
"Requirement already satisfied: protobuf in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from transformers[sentencepiece]) (6.33.1)\n",
|
| 68 |
+
"Requirement already satisfied: aiohappyeyeballs>=2.5.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (2.6.1)\n",
|
| 69 |
+
"Requirement already satisfied: aiosignal>=1.4.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.4.0)\n",
|
| 70 |
+
"Requirement already satisfied: attrs>=17.3.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (25.4.0)\n",
|
| 71 |
+
"Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.8.0)\n",
|
| 72 |
+
"Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (6.7.0)\n",
|
| 73 |
+
"Requirement already satisfied: propcache>=0.2.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (0.4.1)\n",
|
| 74 |
+
"Requirement already satisfied: yarl<2.0,>=1.17.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.22.0)\n",
|
| 75 |
+
"Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from requests>=2.32.2->datasets) (3.4.4)\n",
|
| 76 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from requests>=2.32.2->datasets) (2.5.0)\n",
|
| 77 |
+
"Requirement already satisfied: colorama in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from tqdm>=4.66.3->datasets) (0.4.6)\n",
|
| 78 |
+
"Requirement already satisfied: sniffio>=1.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from anyio->httpx<1.0.0->datasets) (1.3.1)\n",
|
| 79 |
+
"Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from pandas->datasets) (2.9.0.post0)\n",
|
| 80 |
+
"Requirement already satisfied: pytz>=2020.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from pandas->datasets) (2025.2)\n",
|
| 81 |
+
"Requirement already satisfied: tzdata>=2022.7 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from pandas->datasets) (2025.2)\n",
|
| 82 |
+
"Requirement already satisfied: six>=1.5 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
|
| 83 |
+
"Requirement already satisfied: ipywidgets in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (8.1.8)\n",
|
| 84 |
+
"Requirement already satisfied: comm>=0.1.3 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipywidgets) (0.2.3)\n",
|
| 85 |
+
"Requirement already satisfied: ipython>=6.1.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipywidgets) (9.7.0)\n",
|
| 86 |
+
"Requirement already satisfied: traitlets>=4.3.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipywidgets) (5.14.3)\n",
|
| 87 |
+
"Requirement already satisfied: widgetsnbextension~=4.0.14 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipywidgets) (4.0.15)\n",
|
| 88 |
+
"Requirement already satisfied: jupyterlab_widgets~=3.0.15 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipywidgets) (3.0.16)\n",
|
| 89 |
+
"Requirement already satisfied: colorama>=0.4.4 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.4.6)\n",
|
| 90 |
+
"Requirement already satisfied: decorator>=4.3.2 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (5.2.1)\n",
|
| 91 |
+
"Requirement already satisfied: ipython-pygments-lexers>=1.0.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (1.1.1)\n",
|
| 92 |
+
"Requirement already satisfied: jedi>=0.18.1 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.19.2)\n",
|
| 93 |
+
"Requirement already satisfied: matplotlib-inline>=0.1.5 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.2.1)\n",
|
| 94 |
+
"Requirement already satisfied: prompt_toolkit<3.1.0,>=3.0.41 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (3.0.52)\n",
|
| 95 |
+
"Requirement already satisfied: pygments>=2.11.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (2.19.2)\n",
|
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| 139 |
+
"Looking in indexes: https://download.pytorch.org/whl/cu118\n",
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"Requirement already satisfied: torch in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (2.7.1+cu118)\n",
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"Requirement already satisfied: jinja2 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from torch) (3.1.6)\n",
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"Requirement already satisfied: setuptools in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from torch) (80.9.0)\n",
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"Requirement already satisfied: numpy in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from torchvision) (2.3.4)\n",
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| 152 |
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"Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\szyme\\pycharmprojects\\languageprocessinglab\\.venv\\lib\\site-packages (from sympy>=1.13.3->torch) (1.3.0)\n",
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],
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-14T18:39:53.784036Z",
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"start_time": "2025-11-14T18:39:53.693711Z"
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}
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},
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"cell_type": "code",
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"source": [
|
| 173 |
+
"!nvidia-smi\n",
|
| 174 |
+
"import torch\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
|
| 177 |
+
"if torch.cuda.is_available():\n",
|
| 178 |
+
" print(f\"Device: {torch.cuda.get_device_name(0)}\")\n",
|
| 179 |
+
"else:\n",
|
| 180 |
+
" print(\"WARNING: Training on CPU will be very slow!\")"
|
| 181 |
+
],
|
| 182 |
+
"id": "f6d0e86c409e9a9d",
|
| 183 |
+
"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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+
"Fri Nov 14 19:39:53 2025 \n",
|
| 189 |
+
"+-----------------------------------------------------------------------------------------+\n",
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| 190 |
+
"| NVIDIA-SMI 581.42 Driver Version: 581.42 CUDA Version: 13.0 |\n",
|
| 191 |
+
"+-----------------------------------------+------------------------+----------------------+\n",
|
| 192 |
+
"| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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| 193 |
+
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
|
| 194 |
+
"| | | MIG M. |\n",
|
| 195 |
+
"|=========================================+========================+======================|\n",
|
| 196 |
+
"| 0 NVIDIA GeForce RTX 4070 WDDM | 00000000:01:00.0 On | N/A |\n",
|
| 197 |
+
"| 0% 43C P5 25W / 215W | 4740MiB / 12282MiB | 21% Default |\n",
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"| | | N/A |\n",
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+
"+-----------------------------------------+------------------------+----------------------+\n",
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| 200 |
+
"\n",
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+
"+-----------------------------------------------------------------------------------------+\n",
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| 202 |
+
"| Processes: |\n",
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| 203 |
+
"| GPU GI CI PID Type Process name GPU Memory |\n",
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| 204 |
+
"| ID ID Usage |\n",
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+
"|=========================================================================================|\n",
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"| 0 N/A N/A 1416 C+G C:\\Windows\\System32\\dwm.exe N/A |\n",
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"| 0 N/A N/A 2332 C+G ....0.3595.53\\msedgewebview2.exe N/A |\n",
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"| 0 N/A N/A 2736 C+G ...xyewy\\ShellExperienceHost.exe N/A |\n",
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"| 0 N/A N/A 7252 C+G ...8bbwe\\PhoneExperienceHost.exe N/A |\n",
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"| 0 N/A N/A 11540 C+G ...4__8wekyb3d8bbwe\\ms-teams.exe N/A |\n",
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"| 0 N/A N/A 12376 C+G ...l\\Programs\\Opera GX\\opera.exe N/A |\n",
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"| 0 N/A N/A 12668 C+G ...GABYTE\\Control Center\\GCC.exe N/A |\n",
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"| 0 N/A N/A 14740 C+G ...lus\\logioptionsplus_agent.exe N/A |\n",
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"| 0 N/A N/A 14780 C+G ...D\\CNext\\CNext\\AMDRSSrcExt.exe N/A |\n",
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+
"| 0 N/A N/A 15060 C+G C:\\Windows\\explorer.exe N/A |\n",
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+
"| 0 N/A N/A 15140 C+G ...indows\\System32\\ShellHost.exe N/A |\n",
|
| 218 |
+
"| 0 N/A N/A 16828 C+G ..._cw5n1h2txyewy\\SearchHost.exe N/A |\n",
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| 219 |
+
"| 0 N/A N/A 16836 C+G ...y\\StartMenuExperienceHost.exe N/A |\n",
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+
"| 0 N/A N/A 19100 C+G ....0.3595.53\\msedgewebview2.exe N/A |\n",
|
| 221 |
+
"| 0 N/A N/A 19252 C+G ...64__zpdnekdrzrea0\\Spotify.exe N/A |\n",
|
| 222 |
+
"| 0 N/A N/A 20520 C+G ...Next\\CNext\\RadeonSoftware.exe N/A |\n",
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| 223 |
+
"| 0 N/A N/A 20576 C+G ...roadcast\\NVIDIA Broadcast.exe N/A |\n",
|
| 224 |
+
"| 0 N/A N/A 21188 C+G ...cord\\app-1.0.9214\\Discord.exe N/A |\n",
|
| 225 |
+
"| 0 N/A N/A 22276 C+G ...lpaper_engine\\wallpaper64.exe N/A |\n",
|
| 226 |
+
"| 0 N/A N/A 23288 C+G ...l\\Programs\\Opera GX\\opera.exe N/A |\n",
|
| 227 |
+
"| 0 N/A N/A 23576 C+G ...__8yrtsj140pw4g\\app\\Slack.exe N/A |\n",
|
| 228 |
+
"| 0 N/A N/A 23656 C+G ...zcv7bpp5a\\Raycast\\Raycast.exe N/A |\n",
|
| 229 |
+
"| 0 N/A N/A 24616 C+G ...__8yrtsj140pw4g\\app\\Slack.exe N/A |\n",
|
| 230 |
+
"| 0 N/A N/A 24772 C+G ....0.3595.53\\msedgewebview2.exe N/A |\n",
|
| 231 |
+
"| 0 N/A N/A 28520 C+G ...4__8wekyb3d8bbwe\\ms-teams.exe N/A |\n",
|
| 232 |
+
"| 0 N/A N/A 29960 C+G ....0.3595.53\\msedgewebview2.exe N/A |\n",
|
| 233 |
+
"| 0 N/A N/A 31076 C+G ...0_x64__8wekyb3d8bbwe\\Todo.exe N/A |\n",
|
| 234 |
+
"| 0 N/A N/A 31724 C ...gLab\\.venv\\Scripts\\python.exe N/A |\n",
|
| 235 |
+
"| 0 N/A N/A 33752 C+G ...harm 2\\jbr\\bin\\cef_server.exe N/A |\n",
|
| 236 |
+
"| 0 N/A N/A 35756 C+G ...Claude\\app-1.0.332\\claude.exe N/A |\n",
|
| 237 |
+
"| 0 N/A N/A 36000 C+G ...em32\\ApplicationFrameHost.exe N/A |\n",
|
| 238 |
+
"| 0 N/A N/A 36392 C+G C:\\Windows\\System32\\Taskmgr.exe N/A |\n",
|
| 239 |
+
"+-----------------------------------------------------------------------------------------+\n",
|
| 240 |
+
"CUDA available: True\n",
|
| 241 |
+
"Device: NVIDIA GeForce RTX 4070\n"
|
| 242 |
+
]
|
| 243 |
+
}
|
| 244 |
+
],
|
| 245 |
+
"execution_count": 28
|
| 246 |
+
},
|
| 247 |
+
{
|
| 248 |
+
"metadata": {
|
| 249 |
+
"ExecuteTime": {
|
| 250 |
+
"end_time": "2025-11-14T20:21:55.196741Z",
|
| 251 |
+
"start_time": "2025-11-14T20:21:52.456375Z"
|
| 252 |
+
}
|
| 253 |
+
},
|
| 254 |
+
"cell_type": "code",
|
| 255 |
+
"source": [
|
| 256 |
+
"from pathlib import Path\n",
|
| 257 |
+
"from urllib.request import urlretrieve\n",
|
| 258 |
+
"import zipfile\n",
|
| 259 |
+
"import pandas as pd\n",
|
| 260 |
+
"\n",
|
| 261 |
+
"base_dir = Path().resolve()\n",
|
| 262 |
+
"\n",
|
| 263 |
+
"amazon_mobile_reviews_url = \"https://eduds.blob.core.windows.net/nlp/Amazon_Unlocked_Mobile.csv.zip\"\n",
|
| 264 |
+
"filename = \"data/Amazon_Unlocked_Mobile.csv.zip\"\n",
|
| 265 |
+
"data_dir = base_dir / \"data\"\n",
|
| 266 |
+
"\n",
|
| 267 |
+
"zip_path = data_dir / \"Amazon_Unlocked_Mobile.csv.zip\"\n",
|
| 268 |
+
"csv_path = data_dir / \"Amazon_Unlocked_Mobile.csv\"\n",
|
| 269 |
+
"\n",
|
| 270 |
+
"urlretrieve(amazon_mobile_reviews_url, filename)\n",
|
| 271 |
+
"\n",
|
| 272 |
+
"with zipfile.ZipFile(zip_path) as zfile:\n",
|
| 273 |
+
" zfile.extractall(data_dir)\n",
|
| 274 |
+
"\n",
|
| 275 |
+
"df = pd.read_csv(csv_path)"
|
| 276 |
+
],
|
| 277 |
+
"id": "af23e8e3d03e3bfb",
|
| 278 |
+
"outputs": [],
|
| 279 |
+
"execution_count": 63
|
| 280 |
+
},
|
| 281 |
+
{
|
| 282 |
+
"metadata": {
|
| 283 |
+
"ExecuteTime": {
|
| 284 |
+
"end_time": "2025-11-14T20:21:56.503693Z",
|
| 285 |
+
"start_time": "2025-11-14T20:21:56.440111Z"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"cell_type": "code",
|
| 289 |
+
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|
| 290 |
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|
| 291 |
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| 292 |
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|
| 293 |
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],
|
| 294 |
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"id": "27b0bab46de0d18d",
|
| 295 |
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| 296 |
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{
|
| 297 |
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"name": "stdout",
|
| 298 |
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"output_type": "stream",
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| 299 |
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"text": [
|
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|
| 301 |
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"RangeIndex: 413840 entries, 0 to 413839\n",
|
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"Data columns (total 6 columns):\n",
|
| 303 |
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" # Column Non-Null Count Dtype \n",
|
| 304 |
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"--- ------ -------------- ----- \n",
|
| 305 |
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" 0 Product Name 413840 non-null object \n",
|
| 306 |
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" 1 Brand Name 348669 non-null object \n",
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"data": {
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" Product Name Brand Name Price \\\n",
|
| 319 |
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"0 \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7... Samsung 199.99 \n",
|
| 320 |
+
"1 \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7... Samsung 199.99 \n",
|
| 321 |
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"2 \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7... Samsung 199.99 \n",
|
| 322 |
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"3 \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7... Samsung 199.99 \n",
|
| 323 |
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"4 \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7... Samsung 199.99 \n",
|
| 324 |
+
"\n",
|
| 325 |
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" Rating Reviews Review Votes \n",
|
| 326 |
+
"0 5 I feel so LUCKY to have found this used (phone... 1.0 \n",
|
| 327 |
+
"1 4 nice phone, nice up grade from my pantach revu... 0.0 \n",
|
| 328 |
+
"2 5 Very pleased 0.0 \n",
|
| 329 |
+
"3 4 It works good but it goes slow sometimes but i... 0.0 \n",
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"4 4 Great phone to replace my lost phone. The only... 0.0 "
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|
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|
| 350 |
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" <th></th>\n",
|
| 351 |
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" <th>Product Name</th>\n",
|
| 352 |
+
" <th>Brand Name</th>\n",
|
| 353 |
+
" <th>Price</th>\n",
|
| 354 |
+
" <th>Rating</th>\n",
|
| 355 |
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" <th>Reviews</th>\n",
|
| 356 |
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" <th>Review Votes</th>\n",
|
| 357 |
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|
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|
| 359 |
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" <tbody>\n",
|
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|
| 361 |
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" <th>0</th>\n",
|
| 362 |
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" <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
|
| 363 |
+
" <td>Samsung</td>\n",
|
| 364 |
+
" <td>199.99</td>\n",
|
| 365 |
+
" <td>5</td>\n",
|
| 366 |
+
" <td>I feel so LUCKY to have found this used (phone...</td>\n",
|
| 367 |
+
" <td>1.0</td>\n",
|
| 368 |
+
" </tr>\n",
|
| 369 |
+
" <tr>\n",
|
| 370 |
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" <th>1</th>\n",
|
| 371 |
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" <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
|
| 372 |
+
" <td>Samsung</td>\n",
|
| 373 |
+
" <td>199.99</td>\n",
|
| 374 |
+
" <td>4</td>\n",
|
| 375 |
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" <td>nice phone, nice up grade from my pantach revu...</td>\n",
|
| 376 |
+
" <td>0.0</td>\n",
|
| 377 |
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" </tr>\n",
|
| 378 |
+
" <tr>\n",
|
| 379 |
+
" <th>2</th>\n",
|
| 380 |
+
" <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
|
| 381 |
+
" <td>Samsung</td>\n",
|
| 382 |
+
" <td>199.99</td>\n",
|
| 383 |
+
" <td>5</td>\n",
|
| 384 |
+
" <td>Very pleased</td>\n",
|
| 385 |
+
" <td>0.0</td>\n",
|
| 386 |
+
" </tr>\n",
|
| 387 |
+
" <tr>\n",
|
| 388 |
+
" <th>3</th>\n",
|
| 389 |
+
" <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
|
| 390 |
+
" <td>Samsung</td>\n",
|
| 391 |
+
" <td>199.99</td>\n",
|
| 392 |
+
" <td>4</td>\n",
|
| 393 |
+
" <td>It works good but it goes slow sometimes but i...</td>\n",
|
| 394 |
+
" <td>0.0</td>\n",
|
| 395 |
+
" </tr>\n",
|
| 396 |
+
" <tr>\n",
|
| 397 |
+
" <th>4</th>\n",
|
| 398 |
+
" <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
|
| 399 |
+
" <td>Samsung</td>\n",
|
| 400 |
+
" <td>199.99</td>\n",
|
| 401 |
+
" <td>4</td>\n",
|
| 402 |
+
" <td>Great phone to replace my lost phone. The only...</td>\n",
|
| 403 |
+
" <td>0.0</td>\n",
|
| 404 |
+
" </tr>\n",
|
| 405 |
+
" </tbody>\n",
|
| 406 |
+
"</table>\n",
|
| 407 |
+
"</div>"
|
| 408 |
+
]
|
| 409 |
+
},
|
| 410 |
+
"execution_count": 64,
|
| 411 |
+
"metadata": {},
|
| 412 |
+
"output_type": "execute_result"
|
| 413 |
+
}
|
| 414 |
+
],
|
| 415 |
+
"execution_count": 64
|
| 416 |
+
},
|
| 417 |
+
{
|
| 418 |
+
"metadata": {},
|
| 419 |
+
"cell_type": "markdown",
|
| 420 |
+
"source": "## CONFIGURATION\n",
|
| 421 |
+
"id": "a44e589066e2e256"
|
| 422 |
+
},
|
| 423 |
+
{
|
| 424 |
+
"metadata": {
|
| 425 |
+
"ExecuteTime": {
|
| 426 |
+
"end_time": "2025-11-14T18:46:29.881682Z",
|
| 427 |
+
"start_time": "2025-11-14T18:46:29.879491Z"
|
| 428 |
+
}
|
| 429 |
+
},
|
| 430 |
+
"cell_type": "code",
|
| 431 |
+
"source": [
|
| 432 |
+
"DATA_URL = \"https://eduds.blob.core.windows.net/nlp/Amazon_Unlocked_Mobile.csv.zip\"\n",
|
| 433 |
+
"DATA_FILE = \"data/Amazon_Unlocked_Mobile.csv\"\n",
|
| 434 |
+
"MODEL_CHECKPOINT = \"distilbert-base-uncased\"\n",
|
| 435 |
+
"REPO_NAME = \"Floressek/sentiment_classification_from_distillbert\"\n",
|
| 436 |
+
"HUGGING_FACE_TOKEN = \"here input your token\"\n",
|
| 437 |
+
"\n",
|
| 438 |
+
"MAX_REVIEW_LENGTH = 128\n",
|
| 439 |
+
"TEST_SIZE = 0.3\n",
|
| 440 |
+
"BATCH_SIZE = 48\n",
|
| 441 |
+
"BATCH_SIZE_TOKEN = 1000\n",
|
| 442 |
+
"LEARNING_RATE = 2e-5\n",
|
| 443 |
+
"NUM_EPOCHS = 2\n",
|
| 444 |
+
"WEIGHT_DECAY = 0.01\n",
|
| 445 |
+
"SEED = 100"
|
| 446 |
+
],
|
| 447 |
+
"id": "2cf90b8ba07d0a63",
|
| 448 |
+
"outputs": [],
|
| 449 |
+
"execution_count": 45
|
| 450 |
+
},
|
| 451 |
+
{
|
| 452 |
+
"metadata": {},
|
| 453 |
+
"cell_type": "markdown",
|
| 454 |
+
"source": "## Data cleaning\n",
|
| 455 |
+
"id": "fbce9d6f95c91c27"
|
| 456 |
+
},
|
| 457 |
+
{
|
| 458 |
+
"metadata": {
|
| 459 |
+
"ExecuteTime": {
|
| 460 |
+
"end_time": "2025-11-14T18:46:31.582547Z",
|
| 461 |
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"start_time": "2025-11-14T18:46:31.574356Z"
|
| 462 |
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}
|
| 463 |
+
},
|
| 464 |
+
"cell_type": "code",
|
| 465 |
+
"source": [
|
| 466 |
+
"df = df.drop(columns=[\"Brand Name\", \"Price\", \"Review Votes\", \"Product Name\"])\n",
|
| 467 |
+
"df.head()"
|
| 468 |
+
],
|
| 469 |
+
"id": "d6daa3969babb120",
|
| 470 |
+
"outputs": [
|
| 471 |
+
{
|
| 472 |
+
"data": {
|
| 473 |
+
"text/plain": [
|
| 474 |
+
" Rating Reviews\n",
|
| 475 |
+
"0 5 I feel so LUCKY to have found this used (phone...\n",
|
| 476 |
+
"1 4 nice phone, nice up grade from my pantach revu...\n",
|
| 477 |
+
"2 5 Very pleased\n",
|
| 478 |
+
"3 4 It works good but it goes slow sometimes but i...\n",
|
| 479 |
+
"4 4 Great phone to replace my lost phone. The only..."
|
| 480 |
+
],
|
| 481 |
+
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|
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|
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|
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|
| 485 |
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|
| 486 |
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|
| 487 |
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|
| 488 |
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|
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|
| 490 |
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|
| 491 |
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"\n",
|
| 492 |
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|
| 493 |
+
" text-align: right;\n",
|
| 494 |
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" }\n",
|
| 495 |
+
"</style>\n",
|
| 496 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 497 |
+
" <thead>\n",
|
| 498 |
+
" <tr style=\"text-align: right;\">\n",
|
| 499 |
+
" <th></th>\n",
|
| 500 |
+
" <th>Rating</th>\n",
|
| 501 |
+
" <th>Reviews</th>\n",
|
| 502 |
+
" </tr>\n",
|
| 503 |
+
" </thead>\n",
|
| 504 |
+
" <tbody>\n",
|
| 505 |
+
" <tr>\n",
|
| 506 |
+
" <th>0</th>\n",
|
| 507 |
+
" <td>5</td>\n",
|
| 508 |
+
" <td>I feel so LUCKY to have found this used (phone...</td>\n",
|
| 509 |
+
" </tr>\n",
|
| 510 |
+
" <tr>\n",
|
| 511 |
+
" <th>1</th>\n",
|
| 512 |
+
" <td>4</td>\n",
|
| 513 |
+
" <td>nice phone, nice up grade from my pantach revu...</td>\n",
|
| 514 |
+
" </tr>\n",
|
| 515 |
+
" <tr>\n",
|
| 516 |
+
" <th>2</th>\n",
|
| 517 |
+
" <td>5</td>\n",
|
| 518 |
+
" <td>Very pleased</td>\n",
|
| 519 |
+
" </tr>\n",
|
| 520 |
+
" <tr>\n",
|
| 521 |
+
" <th>3</th>\n",
|
| 522 |
+
" <td>4</td>\n",
|
| 523 |
+
" <td>It works good but it goes slow sometimes but i...</td>\n",
|
| 524 |
+
" </tr>\n",
|
| 525 |
+
" <tr>\n",
|
| 526 |
+
" <th>4</th>\n",
|
| 527 |
+
" <td>4</td>\n",
|
| 528 |
+
" <td>Great phone to replace my lost phone. The only...</td>\n",
|
| 529 |
+
" </tr>\n",
|
| 530 |
+
" </tbody>\n",
|
| 531 |
+
"</table>\n",
|
| 532 |
+
"</div>"
|
| 533 |
+
]
|
| 534 |
+
},
|
| 535 |
+
"execution_count": 46,
|
| 536 |
+
"metadata": {},
|
| 537 |
+
"output_type": "execute_result"
|
| 538 |
+
}
|
| 539 |
+
],
|
| 540 |
+
"execution_count": 46
|
| 541 |
+
},
|
| 542 |
+
{
|
| 543 |
+
"metadata": {
|
| 544 |
+
"ExecuteTime": {
|
| 545 |
+
"end_time": "2025-11-14T18:46:34.926211Z",
|
| 546 |
+
"start_time": "2025-11-14T18:46:33.614096Z"
|
| 547 |
+
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|
| 548 |
+
},
|
| 549 |
+
"cell_type": "code",
|
| 550 |
+
"source": [
|
| 551 |
+
"from datasets import Dataset\n",
|
| 552 |
+
"\n",
|
| 553 |
+
"dataset = Dataset.from_pandas(df)\n",
|
| 554 |
+
"\n",
|
| 555 |
+
"dataset = dataset.filter(\n",
|
| 556 |
+
" lambda x: (\n",
|
| 557 |
+
" x[\"Reviews\"] is not None\n",
|
| 558 |
+
" and len(x[\"Reviews\"].split()) < MAX_REVIEW_LENGTH\n",
|
| 559 |
+
" and x[\"Rating\"] in [1, 5] # skrajnosci do binary classification\n",
|
| 560 |
+
" )\n",
|
| 561 |
+
")\n",
|
| 562 |
+
"\n",
|
| 563 |
+
"dataset_split = dataset.train_test_split(test_size=TEST_SIZE, seed=SEED)"
|
| 564 |
+
],
|
| 565 |
+
"id": "40a44a79b98aa996",
|
| 566 |
+
"outputs": [
|
| 567 |
+
{
|
| 568 |
+
"data": {
|
| 569 |
+
"text/plain": [
|
| 570 |
+
"Filter: 0%| | 0/413840 [00:00<?, ? examples/s]"
|
| 571 |
+
],
|
| 572 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 573 |
+
"version_major": 2,
|
| 574 |
+
"version_minor": 0,
|
| 575 |
+
"model_id": "5023c01162324b84afb19ec45a970c56"
|
| 576 |
+
}
|
| 577 |
+
},
|
| 578 |
+
"metadata": {},
|
| 579 |
+
"output_type": "display_data",
|
| 580 |
+
"jetTransient": {
|
| 581 |
+
"display_id": null
|
| 582 |
+
}
|
| 583 |
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|
| 584 |
+
],
|
| 585 |
+
"execution_count": 47
|
| 586 |
+
},
|
| 587 |
+
{
|
| 588 |
+
"metadata": {},
|
| 589 |
+
"cell_type": "markdown",
|
| 590 |
+
"source": "### Tokenization",
|
| 591 |
+
"id": "f1cb6bd3ccecc50d"
|
| 592 |
+
},
|
| 593 |
+
{
|
| 594 |
+
"metadata": {
|
| 595 |
+
"ExecuteTime": {
|
| 596 |
+
"end_time": "2025-11-14T18:46:37.851802Z",
|
| 597 |
+
"start_time": "2025-11-14T18:46:37.100084Z"
|
| 598 |
+
}
|
| 599 |
+
},
|
| 600 |
+
"cell_type": "code",
|
| 601 |
+
"source": [
|
| 602 |
+
"from datasets import DatasetDict\n",
|
| 603 |
+
"from typing import Any\n",
|
| 604 |
+
"from transformers import AutoTokenizer\n",
|
| 605 |
+
"\n",
|
| 606 |
+
"tokenizer = AutoTokenizer.from_pretrained(MODEL_CHECKPOINT)\n",
|
| 607 |
+
"\n",
|
| 608 |
+
"\n",
|
| 609 |
+
"# zostawiamy inputs id z tokenizacji i attention mask dla padding/tresc oraz labels\n",
|
| 610 |
+
"def tokenize_and_label(dataset: DatasetDict, tokenizer: Any):\n",
|
| 611 |
+
" def tokenize_function(example):\n",
|
| 612 |
+
" return tokenizer(example[\"Reviews\"], padding=\"max_length\", truncation=True)\n",
|
| 613 |
+
"\n",
|
| 614 |
+
" def convert_to_binary_label(example):\n",
|
| 615 |
+
" return {'label': [0 if r == 1 else 1 for r in example['Rating']]}\n",
|
| 616 |
+
"\n",
|
| 617 |
+
" tokenized = dataset.map(tokenize_function, batched=True, batch_size=BATCH_SIZE_TOKEN)\n",
|
| 618 |
+
" tokenized = tokenized.map(convert_to_binary_label, batched=True, batch_size=BATCH_SIZE_TOKEN)\n",
|
| 619 |
+
" tokenized = tokenized.remove_columns([\"Reviews\", \"Rating\"])\n",
|
| 620 |
+
"\n",
|
| 621 |
+
" return tokenized"
|
| 622 |
+
],
|
| 623 |
+
"id": "18c877bae841c93e",
|
| 624 |
+
"outputs": [],
|
| 625 |
+
"execution_count": 48
|
| 626 |
+
},
|
| 627 |
+
{
|
| 628 |
+
"metadata": {
|
| 629 |
+
"ExecuteTime": {
|
| 630 |
+
"end_time": "2025-11-14T18:46:41.756880Z",
|
| 631 |
+
"start_time": "2025-11-14T18:46:41.754675Z"
|
| 632 |
+
}
|
| 633 |
+
},
|
| 634 |
+
"cell_type": "code",
|
| 635 |
+
"source": [
|
| 636 |
+
"import numpy as np\n",
|
| 637 |
+
"from evaluate import load\n",
|
| 638 |
+
"\n",
|
| 639 |
+
"\n",
|
| 640 |
+
"def compute_metrics(eval_pred) -> dict:\n",
|
| 641 |
+
" accuracy_metric = load(\"accuracy\")\n",
|
| 642 |
+
" f1_metric = load(\"f1\")\n",
|
| 643 |
+
"\n",
|
| 644 |
+
" logits, labels = eval_pred\n",
|
| 645 |
+
" predictions = np.argmax(logits, axis=-1)\n",
|
| 646 |
+
"\n",
|
| 647 |
+
" accuracy = accuracy_metric.compute(predictions=predictions, references=labels)[\"accuracy\"]\n",
|
| 648 |
+
" f1 = f1_metric.compute(predictions=predictions, references=labels)[\"f1\"]\n",
|
| 649 |
+
"\n",
|
| 650 |
+
" return {\"accuracy\": accuracy, \"f1\": f1}"
|
| 651 |
+
],
|
| 652 |
+
"id": "763efe8d9ac72643",
|
| 653 |
+
"outputs": [],
|
| 654 |
+
"execution_count": 50
|
| 655 |
+
},
|
| 656 |
+
{
|
| 657 |
+
"metadata": {},
|
| 658 |
+
"cell_type": "markdown",
|
| 659 |
+
"source": "## Huggingface - logging and model loading\n",
|
| 660 |
+
"id": "b73061c3bcedc4a2"
|
| 661 |
+
},
|
| 662 |
+
{
|
| 663 |
+
"metadata": {
|
| 664 |
+
"ExecuteTime": {
|
| 665 |
+
"end_time": "2025-11-14T18:46:44.096470Z",
|
| 666 |
+
"start_time": "2025-11-14T18:46:43.924416Z"
|
| 667 |
+
}
|
| 668 |
+
},
|
| 669 |
+
"cell_type": "code",
|
| 670 |
+
"source": [
|
| 671 |
+
"from huggingface_hub import login\n",
|
| 672 |
+
"\n",
|
| 673 |
+
"token = HUGGING_FACE_TOKEN\n",
|
| 674 |
+
"\n",
|
| 675 |
+
"if token:\n",
|
| 676 |
+
" login(token=HUGGING_FACE_TOKEN)\n",
|
| 677 |
+
"else:\n",
|
| 678 |
+
" login()"
|
| 679 |
+
],
|
| 680 |
+
"id": "b439612b50fed578",
|
| 681 |
+
"outputs": [],
|
| 682 |
+
"execution_count": 51
|
| 683 |
+
},
|
| 684 |
+
{
|
| 685 |
+
"metadata": {},
|
| 686 |
+
"cell_type": "markdown",
|
| 687 |
+
"source": "## Train and fine-tuning the model\n",
|
| 688 |
+
"id": "9560b042d0525b99"
|
| 689 |
+
},
|
| 690 |
+
{
|
| 691 |
+
"metadata": {
|
| 692 |
+
"ExecuteTime": {
|
| 693 |
+
"end_time": "2025-11-14T19:29:10.301060Z",
|
| 694 |
+
"start_time": "2025-11-14T18:46:45.568724Z"
|
| 695 |
+
}
|
| 696 |
+
},
|
| 697 |
+
"cell_type": "code",
|
| 698 |
+
"source": [
|
| 699 |
+
"from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
|
| 700 |
+
"\n",
|
| 701 |
+
"\n",
|
| 702 |
+
"def train_model(tokenized_datasets: DatasetDict, tokenizer) -> Trainer:\n",
|
| 703 |
+
" model = AutoModelForSequenceClassification.from_pretrained(\n",
|
| 704 |
+
" MODEL_CHECKPOINT,\n",
|
| 705 |
+
" num_labels=2,\n",
|
| 706 |
+
" )\n",
|
| 707 |
+
"\n",
|
| 708 |
+
" training_args = TrainingArguments(\n",
|
| 709 |
+
" output_dir=REPO_NAME,\n",
|
| 710 |
+
" learning_rate=LEARNING_RATE,\n",
|
| 711 |
+
" per_device_train_batch_size=BATCH_SIZE,\n",
|
| 712 |
+
" per_device_eval_batch_size=BATCH_SIZE,\n",
|
| 713 |
+
" num_train_epochs=NUM_EPOCHS,\n",
|
| 714 |
+
" weight_decay=WEIGHT_DECAY,\n",
|
| 715 |
+
" save_strategy=\"epoch\",\n",
|
| 716 |
+
" eval_strategy=\"epoch\",\n",
|
| 717 |
+
" push_to_hub=True,\n",
|
| 718 |
+
" remove_unused_columns=False,\n",
|
| 719 |
+
" logging_steps=100,\n",
|
| 720 |
+
" fp16=True,\n",
|
| 721 |
+
" )\n",
|
| 722 |
+
"\n",
|
| 723 |
+
" trainer = Trainer(\n",
|
| 724 |
+
" model=model,\n",
|
| 725 |
+
" args=training_args,\n",
|
| 726 |
+
" train_dataset=tokenized_datasets['train'],\n",
|
| 727 |
+
" eval_dataset=tokenized_datasets['test'],\n",
|
| 728 |
+
" processing_class=tokenizer,\n",
|
| 729 |
+
" compute_metrics=compute_metrics\n",
|
| 730 |
+
" )\n",
|
| 731 |
+
"\n",
|
| 732 |
+
" trainer.train()\n",
|
| 733 |
+
"\n",
|
| 734 |
+
" return trainer\n",
|
| 735 |
+
"\n",
|
| 736 |
+
"\n",
|
| 737 |
+
"print(f\"Train size: {len(dataset_split['train'])}\")\n",
|
| 738 |
+
"print(f\"Test size: {len(dataset_split['test'])}\")\n",
|
| 739 |
+
"\n",
|
| 740 |
+
"tokenized_datasets = tokenize_and_label(dataset_split, tokenizer)\n",
|
| 741 |
+
"\n",
|
| 742 |
+
"trainer = train_model(tokenized_datasets, tokenizer)"
|
| 743 |
+
],
|
| 744 |
+
"id": "f2baddc39582ea90",
|
| 745 |
+
"outputs": [
|
| 746 |
+
{
|
| 747 |
+
"name": "stdout",
|
| 748 |
+
"output_type": "stream",
|
| 749 |
+
"text": [
|
| 750 |
+
"Train size: 196375\n",
|
| 751 |
+
"Test size: 84162\n"
|
| 752 |
+
]
|
| 753 |
+
},
|
| 754 |
+
{
|
| 755 |
+
"data": {
|
| 756 |
+
"text/plain": [
|
| 757 |
+
"Map: 0%| | 0/196375 [00:00<?, ? examples/s]"
|
| 758 |
+
],
|
| 759 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 760 |
+
"version_major": 2,
|
| 761 |
+
"version_minor": 0,
|
| 762 |
+
"model_id": "9eca0885f7e941e9ba0225ecab5226b5"
|
| 763 |
+
}
|
| 764 |
+
},
|
| 765 |
+
"metadata": {},
|
| 766 |
+
"output_type": "display_data",
|
| 767 |
+
"jetTransient": {
|
| 768 |
+
"display_id": null
|
| 769 |
+
}
|
| 770 |
+
},
|
| 771 |
+
{
|
| 772 |
+
"data": {
|
| 773 |
+
"text/plain": [
|
| 774 |
+
"Map: 0%| | 0/84162 [00:00<?, ? examples/s]"
|
| 775 |
+
],
|
| 776 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 777 |
+
"version_major": 2,
|
| 778 |
+
"version_minor": 0,
|
| 779 |
+
"model_id": "46e17d5252ff4edba4613665ce78610f"
|
| 780 |
+
}
|
| 781 |
+
},
|
| 782 |
+
"metadata": {},
|
| 783 |
+
"output_type": "display_data",
|
| 784 |
+
"jetTransient": {
|
| 785 |
+
"display_id": null
|
| 786 |
+
}
|
| 787 |
+
},
|
| 788 |
+
{
|
| 789 |
+
"data": {
|
| 790 |
+
"text/plain": [
|
| 791 |
+
"Map: 0%| | 0/196375 [00:00<?, ? examples/s]"
|
| 792 |
+
],
|
| 793 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 794 |
+
"version_major": 2,
|
| 795 |
+
"version_minor": 0,
|
| 796 |
+
"model_id": "b0899afc630842dd9c6de08b7d186a3b"
|
| 797 |
+
}
|
| 798 |
+
},
|
| 799 |
+
"metadata": {},
|
| 800 |
+
"output_type": "display_data",
|
| 801 |
+
"jetTransient": {
|
| 802 |
+
"display_id": null
|
| 803 |
+
}
|
| 804 |
+
},
|
| 805 |
+
{
|
| 806 |
+
"data": {
|
| 807 |
+
"text/plain": [
|
| 808 |
+
"Map: 0%| | 0/84162 [00:00<?, ? examples/s]"
|
| 809 |
+
],
|
| 810 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 811 |
+
"version_major": 2,
|
| 812 |
+
"version_minor": 0,
|
| 813 |
+
"model_id": "669f11f90f5d4ac19919c0ae3772bf7b"
|
| 814 |
+
}
|
| 815 |
+
},
|
| 816 |
+
"metadata": {},
|
| 817 |
+
"output_type": "display_data",
|
| 818 |
+
"jetTransient": {
|
| 819 |
+
"display_id": null
|
| 820 |
+
}
|
| 821 |
+
},
|
| 822 |
+
{
|
| 823 |
+
"name": "stderr",
|
| 824 |
+
"output_type": "stream",
|
| 825 |
+
"text": [
|
| 826 |
+
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
|
| 827 |
+
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
| 828 |
+
]
|
| 829 |
+
},
|
| 830 |
+
{
|
| 831 |
+
"data": {
|
| 832 |
+
"text/plain": [
|
| 833 |
+
"<IPython.core.display.HTML object>"
|
| 834 |
+
],
|
| 835 |
+
"text/html": [
|
| 836 |
+
"\n",
|
| 837 |
+
" <div>\n",
|
| 838 |
+
" \n",
|
| 839 |
+
" <progress value='8184' max='8184' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
| 840 |
+
" [8184/8184 41:33, Epoch 2/2]\n",
|
| 841 |
+
" </div>\n",
|
| 842 |
+
" <table border=\"1\" class=\"dataframe\">\n",
|
| 843 |
+
" <thead>\n",
|
| 844 |
+
" <tr style=\"text-align: left;\">\n",
|
| 845 |
+
" <th>Epoch</th>\n",
|
| 846 |
+
" <th>Training Loss</th>\n",
|
| 847 |
+
" <th>Validation Loss</th>\n",
|
| 848 |
+
" <th>Accuracy</th>\n",
|
| 849 |
+
" <th>F1</th>\n",
|
| 850 |
+
" </tr>\n",
|
| 851 |
+
" </thead>\n",
|
| 852 |
+
" <tbody>\n",
|
| 853 |
+
" <tr>\n",
|
| 854 |
+
" <td>1</td>\n",
|
| 855 |
+
" <td>0.043200</td>\n",
|
| 856 |
+
" <td>0.040534</td>\n",
|
| 857 |
+
" <td>0.989152</td>\n",
|
| 858 |
+
" <td>0.992839</td>\n",
|
| 859 |
+
" </tr>\n",
|
| 860 |
+
" <tr>\n",
|
| 861 |
+
" <td>2</td>\n",
|
| 862 |
+
" <td>0.029900</td>\n",
|
| 863 |
+
" <td>0.035284</td>\n",
|
| 864 |
+
" <td>0.991718</td>\n",
|
| 865 |
+
" <td>0.994545</td>\n",
|
| 866 |
+
" </tr>\n",
|
| 867 |
+
" </tbody>\n",
|
| 868 |
+
"</table><p>"
|
| 869 |
+
]
|
| 870 |
+
},
|
| 871 |
+
"metadata": {},
|
| 872 |
+
"output_type": "display_data",
|
| 873 |
+
"jetTransient": {
|
| 874 |
+
"display_id": "5232456d49be4c01fb108dc77e31f262"
|
| 875 |
+
}
|
| 876 |
+
},
|
| 877 |
+
{
|
| 878 |
+
"data": {
|
| 879 |
+
"text/plain": [
|
| 880 |
+
"Downloading builder script: 0.00B [00:00, ?B/s]"
|
| 881 |
+
],
|
| 882 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 883 |
+
"version_major": 2,
|
| 884 |
+
"version_minor": 0,
|
| 885 |
+
"model_id": "6ea920f26713434d9390bbe9cfa3a896"
|
| 886 |
+
}
|
| 887 |
+
},
|
| 888 |
+
"metadata": {},
|
| 889 |
+
"output_type": "display_data",
|
| 890 |
+
"jetTransient": {
|
| 891 |
+
"display_id": null
|
| 892 |
+
}
|
| 893 |
+
},
|
| 894 |
+
{
|
| 895 |
+
"data": {
|
| 896 |
+
"text/plain": [
|
| 897 |
+
"Downloading builder script: 0.00B [00:00, ?B/s]"
|
| 898 |
+
],
|
| 899 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 900 |
+
"version_major": 2,
|
| 901 |
+
"version_minor": 0,
|
| 902 |
+
"model_id": "b661d468c9394cf7a2f3e161a223adf1"
|
| 903 |
+
}
|
| 904 |
+
},
|
| 905 |
+
"metadata": {},
|
| 906 |
+
"output_type": "display_data",
|
| 907 |
+
"jetTransient": {
|
| 908 |
+
"display_id": null
|
| 909 |
+
}
|
| 910 |
+
},
|
| 911 |
+
{
|
| 912 |
+
"data": {
|
| 913 |
+
"text/plain": [
|
| 914 |
+
"Downloading builder script: 0.00B [00:00, ?B/s]"
|
| 915 |
+
],
|
| 916 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 917 |
+
"version_major": 2,
|
| 918 |
+
"version_minor": 0,
|
| 919 |
+
"model_id": "e755ba88b1e4457b8bab9944d4b1412f"
|
| 920 |
+
}
|
| 921 |
+
},
|
| 922 |
+
"metadata": {},
|
| 923 |
+
"output_type": "display_data",
|
| 924 |
+
"jetTransient": {
|
| 925 |
+
"display_id": null
|
| 926 |
+
}
|
| 927 |
+
}
|
| 928 |
+
],
|
| 929 |
+
"execution_count": 52
|
| 930 |
+
},
|
| 931 |
+
{
|
| 932 |
+
"metadata": {},
|
| 933 |
+
"cell_type": "markdown",
|
| 934 |
+
"source": "## Testing the newly fine-tuned model\n",
|
| 935 |
+
"id": "195c0e81bf298c1c"
|
| 936 |
+
},
|
| 937 |
+
{
|
| 938 |
+
"metadata": {
|
| 939 |
+
"ExecuteTime": {
|
| 940 |
+
"end_time": "2025-11-14T19:56:16.772843Z",
|
| 941 |
+
"start_time": "2025-11-14T19:56:16.769490Z"
|
| 942 |
+
}
|
| 943 |
+
},
|
| 944 |
+
"cell_type": "code",
|
| 945 |
+
"source": [
|
| 946 |
+
"from transformers import pipeline\n",
|
| 947 |
+
"\n",
|
| 948 |
+
"\n",
|
| 949 |
+
"def create_classifier(model_path=f\"./{REPO_NAME}\"):\n",
|
| 950 |
+
" tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
|
| 951 |
+
" model = AutoModelForSequenceClassification.from_pretrained(model_path)\n",
|
| 952 |
+
"\n",
|
| 953 |
+
" return pipeline(\"text-classification\", model=model, tokenizer=tokenizer)\n",
|
| 954 |
+
"\n",
|
| 955 |
+
"\n",
|
| 956 |
+
"def test_classifier(classifier) -> None:\n",
|
| 957 |
+
" examples = [\n",
|
| 958 |
+
" \"Shame. I wish I hadn't buy it.\",\n",
|
| 959 |
+
" \"Great handset!\",\n",
|
| 960 |
+
" \"Terrible product, waste of money\",\n",
|
| 961 |
+
" \"Best phone ever, highly recommend!\"\n",
|
| 962 |
+
" ]\n",
|
| 963 |
+
"\n",
|
| 964 |
+
" for text in examples:\n",
|
| 965 |
+
" result = classifier(text)\n",
|
| 966 |
+
" print(f\"Text: {text}\")\n",
|
| 967 |
+
" print(f\"Result: {result}\\n\")\n"
|
| 968 |
+
],
|
| 969 |
+
"id": "c1ce352b5ebe328c",
|
| 970 |
+
"outputs": [],
|
| 971 |
+
"execution_count": 53
|
| 972 |
+
},
|
| 973 |
+
{
|
| 974 |
+
"metadata": {},
|
| 975 |
+
"cell_type": "markdown",
|
| 976 |
+
"source": "### Eval metrics",
|
| 977 |
+
"id": "d4c5787afb9e6ae7"
|
| 978 |
+
},
|
| 979 |
+
{
|
| 980 |
+
"metadata": {
|
| 981 |
+
"ExecuteTime": {
|
| 982 |
+
"end_time": "2025-11-14T20:06:29.136185Z",
|
| 983 |
+
"start_time": "2025-11-14T20:03:59.964777Z"
|
| 984 |
+
}
|
| 985 |
+
},
|
| 986 |
+
"cell_type": "code",
|
| 987 |
+
"source": [
|
| 988 |
+
"from transformers import AutoModelForSequenceClassification\n",
|
| 989 |
+
"from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, precision_recall_curve, auc\n",
|
| 990 |
+
"import numpy as np\n",
|
| 991 |
+
"import torch\n",
|
| 992 |
+
"\n",
|
| 993 |
+
"model_path = f\"./{REPO_NAME}\"\n",
|
| 994 |
+
"model = AutoModelForSequenceClassification.from_pretrained(model_path)\n",
|
| 995 |
+
"print(model)\n",
|
| 996 |
+
"\n",
|
| 997 |
+
"preds = trainer.predict(tokenized_datasets[\"test\"])\n",
|
| 998 |
+
"logits = preds.predictions\n",
|
| 999 |
+
"y_true = preds.label_ids\n",
|
| 1000 |
+
"y_pred = np.argmax(logits, axis=-1)\n",
|
| 1001 |
+
"probs = torch.softmax(torch.from_numpy(logits), dim=-1).numpy()[:, 1]\n",
|
| 1002 |
+
"\n",
|
| 1003 |
+
"cm = confusion_matrix(y_true, y_pred)\n",
|
| 1004 |
+
"tn, fp, fn, tp = cm.ravel()\n",
|
| 1005 |
+
"roc_auc = roc_auc_score(y_true, probs)\n",
|
| 1006 |
+
"prec_curve, rec_curve, _ = precision_recall_curve(y_true, probs)\n",
|
| 1007 |
+
"pr_auc = auc(rec_curve, prec_curve)\n",
|
| 1008 |
+
"report = classification_report(y_true, y_pred, digits=4)\n",
|
| 1009 |
+
"\n",
|
| 1010 |
+
"print(\"Confusion matrix:\")\n",
|
| 1011 |
+
"print(cm)\n",
|
| 1012 |
+
"print(f\"TP={tp} FP={fp} TN={tn} FN={fn}\")\n",
|
| 1013 |
+
"print(f\"ROC-AUC={roc_auc:.4f} PR-AUC={pr_auc:.4f}\")\n",
|
| 1014 |
+
"print(report)\n"
|
| 1015 |
+
],
|
| 1016 |
+
"id": "e3308ae685c70928",
|
| 1017 |
+
"outputs": [
|
| 1018 |
+
{
|
| 1019 |
+
"name": "stdout",
|
| 1020 |
+
"output_type": "stream",
|
| 1021 |
+
"text": [
|
| 1022 |
+
"Architecture: DistilBertForSequenceClassification\n",
|
| 1023 |
+
"DistilBertConfig {\n",
|
| 1024 |
+
" \"activation\": \"gelu\",\n",
|
| 1025 |
+
" \"architectures\": [\n",
|
| 1026 |
+
" \"DistilBertForSequenceClassification\"\n",
|
| 1027 |
+
" ],\n",
|
| 1028 |
+
" \"attention_dropout\": 0.1,\n",
|
| 1029 |
+
" \"dim\": 768,\n",
|
| 1030 |
+
" \"dropout\": 0.1,\n",
|
| 1031 |
+
" \"dtype\": \"float32\",\n",
|
| 1032 |
+
" \"hidden_dim\": 3072,\n",
|
| 1033 |
+
" \"initializer_range\": 0.02,\n",
|
| 1034 |
+
" \"max_position_embeddings\": 512,\n",
|
| 1035 |
+
" \"model_type\": \"distilbert\",\n",
|
| 1036 |
+
" \"n_heads\": 12,\n",
|
| 1037 |
+
" \"n_layers\": 6,\n",
|
| 1038 |
+
" \"pad_token_id\": 0,\n",
|
| 1039 |
+
" \"problem_type\": \"single_label_classification\",\n",
|
| 1040 |
+
" \"qa_dropout\": 0.1,\n",
|
| 1041 |
+
" \"seq_classif_dropout\": 0.2,\n",
|
| 1042 |
+
" \"sinusoidal_pos_embds\": false,\n",
|
| 1043 |
+
" \"tie_weights_\": true,\n",
|
| 1044 |
+
" \"transformers_version\": \"4.57.1\",\n",
|
| 1045 |
+
" \"vocab_size\": 30522\n",
|
| 1046 |
+
"}\n",
|
| 1047 |
+
"\n",
|
| 1048 |
+
"Total params: 66,955,010\n",
|
| 1049 |
+
"Trainable params: 66,955,010\n",
|
| 1050 |
+
"Confusion matrix:\n",
|
| 1051 |
+
"[[19922 352]\n",
|
| 1052 |
+
" [ 345 63543]]\n",
|
| 1053 |
+
"TP=63543 FP=352 TN=19922 FN=345\n",
|
| 1054 |
+
"ROC-AUC=0.9983 PR-AUC=0.9994\n",
|
| 1055 |
+
" precision recall f1-score support\n",
|
| 1056 |
+
"\n",
|
| 1057 |
+
" 0 0.9830 0.9826 0.9828 20274\n",
|
| 1058 |
+
" 1 0.9945 0.9946 0.9945 63888\n",
|
| 1059 |
+
"\n",
|
| 1060 |
+
" accuracy 0.9917 84162\n",
|
| 1061 |
+
" macro avg 0.9887 0.9886 0.9887 84162\n",
|
| 1062 |
+
"weighted avg 0.9917 0.9917 0.9917 84162\n",
|
| 1063 |
+
"\n"
|
| 1064 |
+
]
|
| 1065 |
+
}
|
| 1066 |
+
],
|
| 1067 |
+
"execution_count": 62
|
| 1068 |
+
},
|
| 1069 |
+
{
|
| 1070 |
+
"metadata": {},
|
| 1071 |
+
"cell_type": "markdown",
|
| 1072 |
+
"source": "### Testing the model with text samples",
|
| 1073 |
+
"id": "6fcdad5dc49bc568"
|
| 1074 |
+
},
|
| 1075 |
+
{
|
| 1076 |
+
"metadata": {
|
| 1077 |
+
"ExecuteTime": {
|
| 1078 |
+
"end_time": "2025-11-14T19:59:26.564660Z",
|
| 1079 |
+
"start_time": "2025-11-14T19:59:26.258924Z"
|
| 1080 |
+
}
|
| 1081 |
+
},
|
| 1082 |
+
"cell_type": "code",
|
| 1083 |
+
"source": [
|
| 1084 |
+
"# Pomijam bo mialem w trakcie uczenia eval wlaczony\n",
|
| 1085 |
+
"# eval_results = trainer.evaluate()\n",
|
| 1086 |
+
"# print(f\"Evaluation results: {eval_results}\")\n",
|
| 1087 |
+
"\n",
|
| 1088 |
+
"print(\"Testing the fine-tuned model:\")\n",
|
| 1089 |
+
"classifier = create_classifier()\n",
|
| 1090 |
+
"test_classifier(classifier)"
|
| 1091 |
+
],
|
| 1092 |
+
"id": "993ea8f31d059b8d",
|
| 1093 |
+
"outputs": [
|
| 1094 |
+
{
|
| 1095 |
+
"name": "stderr",
|
| 1096 |
+
"output_type": "stream",
|
| 1097 |
+
"text": [
|
| 1098 |
+
"Device set to use cuda:0\n"
|
| 1099 |
+
]
|
| 1100 |
+
},
|
| 1101 |
+
{
|
| 1102 |
+
"name": "stdout",
|
| 1103 |
+
"output_type": "stream",
|
| 1104 |
+
"text": [
|
| 1105 |
+
"Testing the fine-tuned model:\n",
|
| 1106 |
+
"Text: Shame. I wish I hadn't buy it.\n",
|
| 1107 |
+
"Result: [{'label': 'LABEL_0', 'score': 0.9975292086601257}]\n",
|
| 1108 |
+
"\n",
|
| 1109 |
+
"Text: Great handset!\n",
|
| 1110 |
+
"Result: [{'label': 'LABEL_1', 'score': 0.9996094107627869}]\n",
|
| 1111 |
+
"\n",
|
| 1112 |
+
"Text: Terrible product, waste of money\n",
|
| 1113 |
+
"Result: [{'label': 'LABEL_0', 'score': 0.998723566532135}]\n",
|
| 1114 |
+
"\n",
|
| 1115 |
+
"Text: Best phone ever, highly recommend!\n",
|
| 1116 |
+
"Result: [{'label': 'LABEL_1', 'score': 0.9996873140335083}]\n",
|
| 1117 |
+
"\n"
|
| 1118 |
+
]
|
| 1119 |
+
}
|
| 1120 |
+
],
|
| 1121 |
+
"execution_count": 58
|
| 1122 |
+
}
|
| 1123 |
+
],
|
| 1124 |
+
"metadata": {
|
| 1125 |
+
"kernelspec": {
|
| 1126 |
+
"display_name": "Python 3",
|
| 1127 |
+
"language": "python",
|
| 1128 |
+
"name": "python3"
|
| 1129 |
+
},
|
| 1130 |
+
"language_info": {
|
| 1131 |
+
"codemirror_mode": {
|
| 1132 |
+
"name": "ipython",
|
| 1133 |
+
"version": 2
|
| 1134 |
+
},
|
| 1135 |
+
"file_extension": ".py",
|
| 1136 |
+
"mimetype": "text/x-python",
|
| 1137 |
+
"name": "python",
|
| 1138 |
+
"nbconvert_exporter": "python",
|
| 1139 |
+
"pygments_lexer": "ipython2",
|
| 1140 |
+
"version": "2.7.6"
|
| 1141 |
+
}
|
| 1142 |
+
},
|
| 1143 |
+
"nbformat": 4,
|
| 1144 |
+
"nbformat_minor": 5
|
| 1145 |
+
}
|
data/Amazon_Unlocked_Mobile.csv
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:097abeefe303816e0f5e9c9ff380adb25910cf46307d2c104911eae8d0304e76
|
| 3 |
+
size 131879567
|
data/Amazon_Unlocked_Mobile.csv.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:801af84059ea0c660aa082b0f07363fcefe224827ba7231248ecc69d95a739f3
|
| 3 |
+
size 34182461
|