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avidale/pseudo-ppl.ipynb

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"id": "FwrKkQ0ThztB",
"outputId": "5d4289a1-a720-4922-f210-e02fa6c30b15"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c0001c65ebdc4740901dcc23b1f8f97e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/632 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5613b8cb890e4935b1a5b1eaae6fa517",
"version_minor": 0,
"version_major": 2
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"text/plain": [
"Downloading: 0%| | 0.00/45.5M [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at cointegrated/rubert-tiny were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\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"
]
},
{
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},
{
"cell_type": "markdown",
"source": [
"The function below calculates average probability of each token in the sentence given all the other tokens. \n",
"\n",
"In masked language models, this does not amount to the total probability of the whole sentence (the conditional probabilities do not cancel each other out), but it is still a useful measure of a \"naturallness\" of a sentence (for more details read e.g. https://arxiv.org/abs/1910.14659). "
],
"metadata": {
"id": "ZKFwnnaZkRyk"
}
},
{
"cell_type": "code",
"source": [
"def score(model, tokenizer, sentence):\n",
" tensor_input = tokenizer.encode(sentence, return_tensors='pt')\n",
" repeat_input = tensor_input.repeat(tensor_input.size(-1)-2, 1)\n",
" mask = torch.ones(tensor_input.size(-1) - 1).diag(1)[:-2]\n",
" masked_input = repeat_input.masked_fill(mask == 1, tokenizer.mask_token_id)\n",
" labels = repeat_input.masked_fill( masked_input != tokenizer.mask_token_id, -100)\n",
" with torch.inference_mode():\n",
" loss = model(masked_input, labels=labels).loss\n",
" return np.exp(loss.item())"
],
"metadata": {
"id": "hsaaliVuiCq3"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"This works for English..."
],
"metadata": {
"id": "zni50_FgkKJR"
}
},
{
"cell_type": "code",
"source": [
"print(score(sentence='London is the capital of Great Britain.', model=model, tokenizer=tokenizer)) \n",
"# 4.541251105675365\n",
"print(score(sentence='London is the capital of South America.', model=model, tokenizer=tokenizer)) \n",
"# 6.162017238332462"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cpS4FiCyiLd8",
"outputId": "2ee9a372-dc38-49c9-c497-c5ecc4048b9e"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"4.541251105675365\n",
"6.162017238332462\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"... and for Russian as well."
],
"metadata": {
"id": "_4DBab6nkMu_"
}
},
{
"cell_type": "code",
"source": [
"print(score(sentence='Москва — столица России.', model=model, tokenizer=tokenizer)) \n",
"# 31.256664229638908\n",
"print(score(sentence='Москва — столица Швейцарии.', model=model, tokenizer=tokenizer)) \n",
"# 136.21085923946333"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6woKCGzQj-7V",
"outputId": "b4aa44fd-a279-43d8-e0e8-6d84dec490fe"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"31.256664229638908\n",
"136.21085923946333\n"
]
}
]
}
]
}
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