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"cells": [
{
"cell_type": "markdown",
"source": [
"Zero-shot and few-shot learning en un problema de clasificación con instruction-tuned LLM\n",
"===================================================================\n",
"\n",
"Los grandes modelos de lenguaje exhiben grandes habilidades en zero-shot learning. Sin embargo, los resultados dependen mucho de la capacidad del modelo, y de la ténica que utilicemos para resolver el problema.\n",
"\n",
"En este ejemplo, utizaremos un modelo de lenguaje para resolver el problema de clasificación de tweets sin entrenar ningún modelo (zero-shot)."
],
"metadata": {
"id": "g4lLoquNL-pk"
}
},
{
"cell_type": "markdown",
"source": [
"## Introdución\n",
"\n",
"Los grandes modelos de lenguaje son capaces de resolver problemas de clasificación al utilizar determinadas estructuras del idioma. Algunos modelos de lenguaje están especificamente entrenados para seguir instrucciones, los cuales los hace muy útiles a la hora de implementar zero-shot or few-shot learning.\n",
"\n",
"En este ejemplo, veremos como utilizar un modelo de aprendizaje automático entrenado de esta forma para resolver problemas de clasificación."
],
"metadata": {
"id": "7rNqhA9ZMbfu"
}
},
{
"cell_type": "markdown",
"source": [
"### Para ejecutar este notebook\n",
"\n",
"Para ejecutar este notebook, instale las siguientes librerias:"
],
"metadata": {
"id": "iMn_2ecENFcP"
}
},
{
"cell_type": "code",
"source": [
"!wget https://raw.githubusercontent.com/santiagxf/M72109/master/NLP/Datasets/mascorpus/tweets_marketing.csv \\\n",
" --quiet --no-clobber --directory-prefix ./Datasets/mascorpus/\n",
"\n",
"!pip -q install transformers[torch] accelerate datasets evaluate"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fb1pmFc6JuCO",
"outputId": "bb2fdc22-8672-41af-8b64-8a48bb50d7b4"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### Verificando el hardware disponible"
],
"metadata": {
"id": "-u7dKENbNQDn"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
"\n",
"print(\"Este notebook se está ejecutando en\", device)"
],
"metadata": {
"id": "TRTV_uN7NPkV",
"outputId": "beac8237-4079-462b-c41f-c200ce2d1f85",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Este notebook se está ejecutando en cuda\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Iniciamos session en HF:"
],
"metadata": {
"id": "77Ba9zYx2yR1"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
"191b6a790b004003b41bd7a88fc1f94a",
"a1a0fd41726c433c969736c3f8425671",
"6932beb012434ec894a532af62fbd0ad",
"41d64a9993e84221b8357bef021d25e3",
"293b2363ffe844cb97480a4c18b76159",
"e344c93aab9745faa58f1f6cf98f9c20",
"6fbc8e4b384343aca4c252722f2be8ce",
"427b286055494cb1b7306cd5e7d59172",
"e176dac9c50d444580f39a95cd37f3fc",
"a55a9420f90e4aaa9ca8065c11016d3b",
"2d62d2364f0a4df2bb31dc5eb8d86d28",
"09c497fa25224a13bdb1f4dda36b4a84",
"615fc3e533b8409a82f5d3d35a7b20bf",
"d14c0a3b8b014349b427dcd5fe268c35",
"7395cbf74e2f4102bb9bf3a1057b273d",
"073e843a02c9471490d7cc8b50e22df4",
"564b1bbf8f5a4f7895bb8a46a8c7c5e2",
"6d8abf09e6ee44639c7e0069029dccd0",
"f50161df513247dfa99f610534a373cb",
"9422c5fd9ad24ab59516dff2d7df3251"
]
},
"id": "UPESAn1b2sBT",
"outputId": "bf13b217-4a4b-45c1-d8c1-81b1ad50d420"
},
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value=' ' + role + '\\n' + message['content'] | trim + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\\n'}}{% endif %}\""
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"messages = [\n",
" {\"role\": \"user\", \"content\": \"¿Quién eres?\"},\n",
"]\n",
"\n",
"prompt = gemma.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
"print(\"\\n--- Prompt generado ---\\n\")\n",
"print(prompt)\n",
"\n",
"print(\"\\n--- Respuesta del modelo ---\\n\")\n",
"print(gemma(prompt)[0][\"generated_text\"])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "J_9wWtlF4sgE",
"outputId": "ca8d114d-7992-4620-a5b4-b42411bec47c"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"--- Prompt generado ---\n",
"\n",
"user\n",
"¿Quién eres?\n",
"model\n",
"\n",
"\n",
"--- Respuesta del modelo ---\n",
"\n",
"user\n",
"¿Quién eres?\n",
"model\n",
"Soy un modelo lingüístico avanzado. Soy un modelo lingüístico que fue diseñada para ayudar a las personas a comprender el lenguaje y a generar lenguaje.\n",
"\n",
"¿Qué puedo hacer para ayudarte hoy?\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### Zero-shot learning\n",
"\n",
"En general, para resolver cualquier tarea necesitaremos generar un texto o prompt que haga que el modelo genere texto que resuelve el problema de negocio:"
],
"metadata": {
"id": "aWMay5GFQOUz"
}
},
{
"cell_type": "code",
"source": [
"prompt = \"Decide whether the following product review's sentiment is positive, neutral, or negative.\\n\\nProduct review:\\n{}\\nSentiment:\"\n",
"print(prompt)\n"
],
"metadata": {
"id": "bgfDCa41y9Xw",
"outputId": "ebda80d0-424a-4632-aa5b-d292e90769ea",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Decide whether the following product review's sentiment is positive, neutral, or negative.\n",
"\n",
"Product review:\n",
"{}\n",
"Sentiment:\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Ahora, supongamos que nuestro texto es como sigue:"
],
"metadata": {
"id": "XfUTA4BAQzb4"
}
},
{
"cell_type": "code",
"source": [
"sample = \"The reactivity of your team has been good...ish. However, the overal experience is questionable.\""
],
"metadata": {
"id": "u9uDscSpzT6r"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Podemos generar la respuesta del modelo como sigue:"
],
"metadata": {
"id": "kw7KxHZEQ5AB"
}
},
{
"cell_type": "code",
"source": [
"gemma(prompt.format(sample))"
],
"metadata": {
"id": "0dPyBeeGzMe5",
"outputId": "aaf1b82e-b386-42b9-d050-77e7489a11d3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': \"Decide whether the following product review's sentiment is positive, neutral, or negative.\\n\\nProduct review:\\nThe reactivity of your team has been good...ish. However, the overal experience is questionable.\\nSentiment: Neutral\"}]"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"source": [
"### Few-shot learning\n",
"\n",
"En few-shot learning, intentaremos generar un prompt en el cual multiples ejemplos se mencionan antes de solicitarle al modelo que complete el prompt:"
],
"metadata": {
"id": "-mIntNQ8RVaE"
}
},
{
"cell_type": "code",
"source": [
"base_prompt = \"Decide whether the following product reviews' sentiment is positive, neutral, or negative.\"\n",
"examples = [\n",
" (\n",
" \"I love my new chess board!\",\n",
" \"positive\"\n",
" ),\n",
" (\n",
" \"Not what I expected but I guess it'll do\",\n",
" \"neutral\"\n",
" ),\n",
" (\n",
" \"I'm so disappointed. The product seemed much better on the website\",\n",
" \"negative\"\n",
" )\n",
"]"
],
"metadata": {
"id": "DFB8c85JzBLO"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Podemos generar nuestro prompt entonces como sigue:"
],
"metadata": {
"id": "jykOQBD_RnJd"
}
},
{
"cell_type": "code",
"source": [
"def generate_prompt(base_prompt, examples, sample_name, prediction_name, sample=None):\n",
" prompt = base_prompt\n",
" for example in examples:\n",
" prompt += f\"\\n\\n{sample_name}:\\n'{example[0]}'\\n{prediction_name}:\\n{example[1]}\"\n",
"\n",
" prompt += f\"\\n\\n{sample_name}:\\n'{{}}'\\n{prediction_name}:\\n\"\n",
"\n",
" if sample:\n",
" return prompt.format(sample)\n",
" return prompt"
],
"metadata": {
"id": "a3Sk8nTkRqTU"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prompt = generate_prompt(base_prompt, examples, \"Product review\", \"Sentiment\", sample)\n",
"print(prompt)"
],
"metadata": {
"id": "9chi49we0B1r",
"outputId": "8a0c52da-77f0-44e5-e1c2-4451cfe14dd9",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Decide whether the following product reviews' sentiment is positive, neutral, or negative.\n",
"\n",
"Product review:\n",
"'I love my new chess board!'\n",
"Sentiment:\n",
"positive\n",
"\n",
"Product review:\n",
"'Not what I expected but I guess it'll do'\n",
"Sentiment:\n",
"neutral\n",
"\n",
"Product review:\n",
"'I'm so disappointed. The product seemed much better on the website'\n",
"Sentiment:\n",
"negative\n",
"\n",
"Product review:\n",
"'The reactivity of your team has been good...ish. However, the overal experience is questionable.'\n",
"Sentiment:\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Podemos ver como funciona el modelo:"
],
"metadata": {
"id": "6Zf95LEiSg0O"
}
},
{
"cell_type": "code",
"source": [
"gemma(prompt, return_full_text=False)"
],
"metadata": {
"id": "4b7baNqRz8ZO",
"outputId": "c2e03919-1f26-4ce8-cc29-1b614f2cad1d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
"Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'neutral'}]"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "markdown",
"source": [
"Podemos ver que en este caso el modelo genero un texto que es bastante mas largo del que esperabamos. Nos interesaria que solo mencione el sentimiento. Podemos mejorar nuestras instrucciones para solicitar al modelo que realize esto, o simplemente buscar especificamente por tokens en particular:"
],
"metadata": {
"id": "zt3GvXJ3SmBe"
}
},
{
"cell_type": "markdown",
"source": [
"#### Utilizando etiquetas\n",
"\n",
"Primero, utilizaremos el `tokenizer` para generar los IDs correspondiente a los tokens de las etiquetas que disponemos:"
],
"metadata": {
"id": "bfJcGoTpS4YQ"
}
},
{
"cell_type": "code",
"source": [
"target_labels = ['positive', 'neutral', 'negative']\n",
"target_token_ids = [gemma.tokenizer.encode(k, add_special_tokens=False)[0] for k in target_labels]"
],
"metadata": {
"id": "pK65ysbLdHho"
},
"execution_count": 26,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Generaremos una función que solo obtiene la probabilidad más alta para los tokens que están representados por las etiquetas que tenemos. Esta técnica supone que, a pesar de que el modelo genera un texto diferente, la probabilidad de la clase correcta siempre es más alta a lo largo de todo el texto generado."
],
"metadata": {
"id": "ClByqurBTkSP"
}
},
{
"cell_type": "code",
"source": [
"def generate_for_classification(pipeline, prompt, target_labels, target_token_ids):\n",
" input_ids = pipeline.tokenizer(prompt, return_tensors=\"pt\", padding=True).input_ids\n",
" with torch.no_grad():\n",
" outputs = pipeline.model(input_ids.to(pipeline.model.device))\n",
" result = torch.nn.Softmax(dim=-1)(outputs.logits[:, -1, target_token_ids])\n",
"\n",
" predicted_token_ids = torch.argmax(result, axis=1)\n",
" return [target_labels[i] for i in predicted_token_ids], torch.max(result, axis=1)"
],
"metadata": {
"id": "blSLfOt-z0eo"
},
"execution_count": 50,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Ejecutamos la función:"
],
"metadata": {
"id": "EOCCEqetT4In"
}
},
{
"cell_type": "code",
"source": [
"predictions, outputs = generate_for_classification(gemma, prompt, target_labels, target_token_ids)"
],
"metadata": {
"id": "9MdKIShK0nVT"
},
"execution_count": 51,
"outputs": []
},
{
"cell_type": "code",
"source": [
"predictions"
],
"metadata": {
"id": "cmTrA2r-1EPA",
"outputId": "6378381c-b223-46ba-9879-f43b0972b488",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['neutral']"
]
},
"metadata": {},
"execution_count": 39
}
]
},
{
"cell_type": "markdown",
"source": [
"En la función, estamos devolviendo también la variable `result` que contiene la probabilidad de ese token en particular:"
],
"metadata": {
"id": "puar-R69UDwF"
}
},
{
"cell_type": "code",
"source": [
"outputs[0]"
],
"metadata": {
"id": "n5EUAAlb1GMg",
"outputId": "ecf7579c-62e5-4e5e-9006-c3687200b98d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([0.9570], device='cuda:0', dtype=torch.bfloat16)"
]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "markdown",
"source": [
"## Resolviendo un problema de clasificación con few-shot learning"
],
"metadata": {
"id": "4fBw3xtVUdGp"
}
},
{
"cell_type": "markdown",
"source": [
"Cargamos el conjunto de datos:"
],
"metadata": {
"id": "QHS0-NHZUkgo"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"tweets = pd.read_csv('Datasets/mascorpus/tweets_marketing.csv')"
],
"metadata": {
"id": "ccRQQu1D8NJW"
},
"execution_count": 56,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Trataremos de resolver entonces el mismo problema de clasificación con el que veniamos trabajando: clasificar los tweets dependiendo del sector al que pertenecen. Recordemos que tenemos 7 categorias distintas.\n",
"\n",
"Mejoraremos un poco los nombres de las categorías:"
],
"metadata": {
"id": "-Q3vDwCvUv49"
}
},
{
"cell_type": "code",
"source": [
"tweets['SECTOR'] = tweets['SECTOR'].map({\n",
" \"ALIMENTACION\": \"alimentos\",\n",
" \"AUTOMOCION\": \"automobiles\",\n",
" \"BANCA\": \"bancos\",\n",
" \"BEBIDAS\": \"bebidas\",\n",
" \"DEPORTES\": \"deportes\",\n",
" \"RETAIL\": \"supermercados\",\n",
" \"TELCO\": \"telefonía\"\n",
"})"
],
"metadata": {
"id": "9C5aNalCbA6V"
},
"execution_count": 57,
"outputs": []
},
{
"cell_type": "code",
"source": [
"labels = tweets['SECTOR'].unique().tolist()\n",
"labels"
],
"metadata": {
"id": "AZPlYtXJUxVj",
"outputId": "52385678-41c6-4e62-dc4e-8ca4719eb93e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['supermercados',\n",
" 'telefonía',\n",
" 'alimentos',\n",
" 'automobiles',\n",
" 'bancos',\n",
" 'bebidas',\n",
" 'deportes']"
]
},
"metadata": {},
"execution_count": 58
}
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "2WFOptXfWXIv"
}
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train, test = train_test_split(tweets, test_size=0.33, stratify=tweets['SECTOR'])"
],
"metadata": {
"id": "-C44jNRlWXjk"
},
"execution_count": 59,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "cVLXKGldW8GT"
}
},
{
"cell_type": "code",
"source": [
"base_prompt = f\"Clasificar los siguientes tweets según el producto o categoría a la que hacen referencia. Las posibles categorías son: {', '.join(labels)}\""
],
"metadata": {
"id": "3rhmgotMXHg5"
},
"execution_count": 60,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "RCJfNEu2Yekw"
}
},
{
"cell_type": "code",
"source": [
"examples_df = train.groupby('SECTOR').apply(lambda x: x.sample(1)).reset_index(drop=True)\n",
"examples = [(row[1]['TEXTO'], row[1]['SECTOR']) for row in examples_df.iterrows()]"
],
"metadata": {
"id": "kkDJUTreXxxF",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7d7f0f5d-af19-46f4-a595-f9e48ea38587"
},
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/tmp/ipykernel_647/4046959856.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" examples_df = train.groupby('SECTOR').apply(lambda x: x.sample(1)).reset_index(drop=True)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"prompt = generate_prompt(base_prompt, examples, \"Tweet\", \"Categoría\", sample=None)\n",
"print(prompt)"
],
"metadata": {
"id": "JW55zLYiW8c4",
"outputId": "89b9ddce-6ce7-49d5-a33c-397d556d790c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 62,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Clasificar los siguientes tweets según el producto o categoría a la que hacen referencia. Las posibles categorías son: supermercados, telefonía, alimentos, automobiles, bancos, bebidas, deportes\n",
"\n",
"Tweet:\n",
"'Me compré 2 alfajores Milka y uno Pepito😻😏'\n",
"Categoría:\n",
"alimentos\n",
"\n",
"Tweet:\n",
"'#LigaEndesa #ACB Claves de la Jornada 20 del SuperManager KIA https://t.co/uFmGklRkk1 https://t.co/z5oNtmJhLT'\n",
"Categoría:\n",
"automobiles\n",
"\n",
"Tweet:\n",
"'700.000 firmas exigen a BBVA que deje de financiar el calentamiento global y la vulneración de derechos humanos https://t.co/dEn6TERs7Y'\n",
"Categoría:\n",
"bancos\n",
"\n",
"Tweet:\n",
"'me gusta la cruzcampo #Arruinaunacitacon4palabras'\n",
"Categoría:\n",
"bebidas\n",
"\n",
"Tweet:\n",
"'@MiguuelHerrera @adidas holaaaaa @PUMAmexico'\n",
"Categoría:\n",
"deportes\n",
"\n",
"Tweet:\n",
"'\"Alcampo, el supermercado online más barato\" \n",
"#politicadeprecios \n",
"https://t.co/a3wenDb2g8'\n",
"Categoría:\n",
"supermercados\n",
"\n",
"Tweet:\n",
"'@AfectadosBBS Gracias por vuestro apoyo.\n",
"@Movistar @Telefónica me han proporcionado un Samsung Galaxy S5, que ya tengo en mi poder.'\n",
"Categoría:\n",
"telefonía\n",
"\n",
"Tweet:\n",
"'{}'\n",
"Categoría:\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Veamos como funciona con un ejemplo:"
],
"metadata": {
"id": "PtWDpNO5dy7i"
}
},
{
"cell_type": "code",
"source": [
"sample = test.sample(1).iloc[0]"
],
"metadata": {
"id": "lW593Jo9eKLr"
},
"execution_count": 63,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sample['TEXTO']"
],
"metadata": {
"id": "6_Lvi2qGfinq",
"outputId": "d02b2bb1-70cc-41d8-b8c0-8ea5628ec721",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
}
},
"execution_count": 64,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Santander, BBVA y Barclays, líderes en bonos en 2016 https://t.co/LVaV4ic7Fu https://t.co/HZIBdsFSyt'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 64
}
]
},
{
"cell_type": "markdown",
"source": [
"### Utilizando el modelo generativo"
],
"metadata": {
"id": "F9Ucjt8cgCtb"
}
},
{
"cell_type": "code",
"source": [
"gemma(prompt.format(sample['TEXTO']), return_full_text=False)"
],
"metadata": {
"id": "PBaEkWazeyAw",
"outputId": "1c4e339b-45bf-4151-b1a1-d1462ac76a9a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 66,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'bancos'}]"
]
},
"metadata": {},
"execution_count": 66
}
]
}
]
}