"
],
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [18336/18336 43:51, Epoch 3/3]\n",
"
\n",
" \n",
" \n",
" | Epoch | \n",
" Training Loss | \n",
" Validation Loss | \n",
" Accuracy | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" 0.671300 | \n",
" 0.398400 | \n",
" 0.861502 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.161700 | \n",
" 0.342772 | \n",
" 0.917948 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.223200 | \n",
" 0.307898 | \n",
" 0.934391 | \n",
"
\n",
" \n",
"
"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=18336, training_loss=0.35104223199146656, metrics={'train_runtime': 2634.5925, 'train_samples_per_second': 55.678, 'train_steps_per_second': 6.96, 'total_flos': 4731334255614960.0, 'train_loss': 0.35104223199146656, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"#export\n",
"model.save_pretrained(\"./fine_tuned_sentiment_model\")\n",
"tokenizer.save_pretrained(\"./fine_tuned_sentiment_model\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_yqjZpPdPTDf",
"outputId": "f9ceeed8-6f91-48f0-cdf8-64d40ed69ac7"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('./fine_tuned_sentiment_model/tokenizer_config.json',\n",
" './fine_tuned_sentiment_model/special_tokens_map.json',\n",
" './fine_tuned_sentiment_model/vocab.json',\n",
" './fine_tuned_sentiment_model/merges.txt',\n",
" './fine_tuned_sentiment_model/added_tokens.json',\n",
" './fine_tuned_sentiment_model/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"#check performance\n",
"from transformers import pipeline\n",
"\n",
"# Load the fine-tuned model\n",
"classifier = pipeline(\"sentiment-analysis\", model=\"./fine_tuned_sentiment_model\", tokenizer=tokenizer)\n",
"\n",
"# Test with a new sentence\n",
"result = classifier(\"This product is okay!\")\n",
"print(result)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7aVmYPaqPafd",
"outputId": "93000e2d-5589-486f-e369-1565d4065964"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Device set to use cuda:0\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[{'label': 'positive', 'score': 0.9967034459114075}]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Code taken from Hugging Face\n",
"\n",
"from transformers import AutoModelForSequenceClassification\n",
"from transformers import TFAutoModelForSequenceClassification\n",
"from transformers import AutoTokenizer, AutoConfig\n",
"import numpy as np\n",
"from scipy.special import softmax\n",
"# Preprocess text (username and link placeholders)\n",
"def preprocess(text):\n",
" new_text = []\n",
" for t in text.split(\" \"):\n",
" t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
" t = 'http' if t.startswith('http') else t\n",
" new_text.append(t)\n",
" return \" \".join(new_text)\n",
"MODEL = \"./fine_tuned_sentiment_model\"\n",
"# PT\n",
"model = AutoModelForSequenceClassification.from_pretrained(MODEL)\n",
"#model.save_pretrained(MODEL)\n",
"text = \"This product is good\"\n",
"text = preprocess(text)\n",
"encoded_input = tokenizer(text, return_tensors='pt')\n",
"output = model(**encoded_input)\n",
"scores = output[0][0].detach().numpy()\n",
"scores = softmax(scores)\n",
"# # TF\n",
"# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)\n",
"# model.save_pretrained(MODEL)\n",
"# text = \"Covid cases are increasing fast!\"\n",
"# encoded_input = tokenizer(text, return_tensors='tf')\n",
"# output = model(encoded_input)\n",
"# scores = output[0][0].numpy()\n",
"# scores = softmax(scores)\n",
"# Print labels and scores\n",
"ranking = np.argsort(scores)\n",
"ranking = ranking[::-1]\n",
"for i in range(scores.shape[0]):\n",
" l = config.id2label[ranking[i]]\n",
" s = scores[ranking[i]]\n",
" print(f\"{i+1}) {l} {np.round(float(s), 4)}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xI9tjyKtPj3d",
"outputId": "ee67abd1-898f-4920-fd4b-a67ea48bcd6f"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1) positive 0.9989\n",
"2) negative 0.0008\n",
"3) neutral 0.0003\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"import shutil\n",
"\n",
"# Zip the folder\n",
"shutil.make_archive(\"./fine_tuned_sentiment_model\", 'zip', \"./fine_tuned_sentiment_model\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "pnYmuqzrRCXf",
"outputId": "d064f50f-2b20-42f8-b501-41d4fbea214d"
},
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content/fine_tuned_sentiment_model.zip'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 39
}
]
}
]
}