Mantéïa

Exemples/exemple_Generation.py

@@ -10,7 +10,7 @@ def main(args):
 	ds=Dataset('Short_Jokes')
 
 	model       = Model(model_name ='gpt2-medium')
-	text_loader = Create_DataLoader_generation(ds.documents_train[:3000])
+	text_loader = Create_DataLoader_generation(ds.documents_train[:10000],batch_size=32)
 	model.load_tokenizer()
 	model.load_class()
 	model.devices()

Manteia/Model.py

@@ -473,8 +473,8 @@ def Create_DataLoader_predict(inputs,masks,batch_size=16):
 		ss = SequentialSampler(td)
 		return DataLoader(td, sampler=ss, batch_size=batch_size)
 
-def Create_DataLoader_generation(text):
-		return DataLoader(TextDataset(text), batch_size=1, shuffle=True)
+def Create_DataLoader_generation(text,batch_size=16):
+		return DataLoader(TextDataset(text), batch_size=batch_size, shuffle=True)
 
 class TextDataset():
 	def __init__(self,list_texts):

Manteia/__init__.py

@@ -28,7 +28,7 @@
 	reminiscent.
 """
 
-__version__ = "0.0.19"
+__version__ = "0.0.20"
 
 
 from Manteia import Classification

README.md

@@ -27,9 +27,26 @@ Example of use Classification :
 Example of use Generation :
 
 
-	from Manteia.Generation import Generation
-	Generation(seed='What do you do if a bird shits on your car?')
-	If you're a car owner, you're supposed to be able to call the police and have them take the bird off the car.
+	from Manteia.Generation import Generation 
+	from Manteia.Dataset import Dataset
+	from Manteia.Model import *
+
+	ds=Dataset('Short_Jokes')
+
+	model       = Model(model_name ='gpt2-medium')
+	text_loader = Create_DataLoader_generation(ds.documents_train[:10000],batch_size=32)
+	model.load_tokenizer()
+	model.load_class()
+	model.devices()
+	model.configuration(text_loader)
+
+	gn=Generation(model)
+
+	gn.model.fit_generation(text_loader)
+	output      = model.predict_generation('What did you expect ?')
+	output_text = decode_text(output,model.tokenizer)
+	print(output_text)
+
 
 [Documentation](https://manteia.readthedocs.io/en/latest/#)
 [Pypi](https://pypi.org/project/Manteia/)

notebook/notebook_Manteia_classification1.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading roberta tokenizer...\n",
+      "Loading roberta class...\n",
+      "There are 2 GPU(s) available.\n",
+      "We will use the GPU: GeForce RTX 2080 Ti\n",
+      "\n",
+      "======== Epoch 1 / 4 ========\n",
+      "Training :\n",
+      "                                                                          \n",
+      "  Average training loss: 0.67\n",
+      "  Training epoch took: 0:00:00\n",
+      "Validation :\n",
+      "                                                                          \n",
+      "  Accuracy: 0.00\n",
+      "  Validation took: 0:00:00\n",
+      "\n",
+      "======== Epoch 2 / 4 ========\n",
+      "Training :\n",
+      "                                                                          \n",
+      "  Average training loss: 0.68\n",
+      "  Training epoch took: 0:00:00\n",
+      "Validation :\n",
+      "                                                                          \n",
+      "  Accuracy: 0.00\n",
+      "  Validation took: 0:00:00\n",
+      "\n",
+      "======== Epoch 3 / 4 ========\n",
+      "Training :\n",
+      "                                                                          \n",
+      "  Average training loss: 0.66\n",
+      "  Training epoch took: 0:00:00\n",
+      "Validation :\n",
+      "                                                                          \n",
+      "  Accuracy: 0.00\n",
+      "  Validation took: 0:00:00\n",
+      "\n",
+      "======== Epoch 4 / 4 ========\n",
+      "Training :\n",
+      "                                                                          \n",
+      "  Average training loss: 0.63\n",
+      "  Training epoch took: 0:00:00\n",
+      "Validation :\n",
+      "                                                                          \n",
+      "  Accuracy: 0.00\n",
+      "  Validation took: 0:00:00\n",
+      "\n",
+      "Training complete!\n",
+      "['not funny', 'not funny']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from Manteia.Classification import Classification \n",
+    "from Manteia.Model import Model \n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "documents = [\n",
+    "\t\t\t'  !?? What do you call a potato in space? Spudnik:::13 ;;    //   ',\n",
+    "\t\t\t'What should you do before criticizing Pac-Man? WAKA WAKA WAKA mile in his shoe.',\n",
+    "\t\t\t'What did Arnold Schwarzenegger say at the abortion clinic? Hasta last vista, baby.',\n",
+    "\t\t\t'Why do you never see elephants hiding in trees? \\'Cause they are freaking good at it',\n",
+    "\t\t\t'My son just got a tattoo of a heart, a spade, a club, and a diamond, all without my permission. I guess I\\'ll deal with him later.',\n",
+    "\t\t\t'Mom: \"Do you want this?\" Me: \"No.\" Mom: \"Ok I\\'ll give it to your brother.\" Me: \"No I want it.\"',\n",
+    "\t\t\t'Ibuprofen is my favorite headache medicine that also sounds like a reggae professor.',\n",
+    "\t\t\t'INTERVIEWER: Why do you want to work here? ME: *crumbs tumbling from my mouth* Oh, I don\\'t. I was just walking by and saw you had donuts.',\n",
+    "\t\t\t'I\\'ve struggled for years to be above the influence... But I\\'ve never been able to get that high',\n",
+    "\t\t\t'With Facebook, you can stay in touch with people you would otherwise never talk to, but that\\'s only one of the many awful things about it',\n",
+    "\t\t\t]\n",
+    "\t\t\t\n",
+    "labels = [\n",
+    "\t\t\t'funny','not funny','funny','not funny','funny','not funny','not funny','not funny','funny','not funny'\n",
+    "\t\t\t]\n",
+    "\t\t\t\n",
+    "model = Model(model_name ='roberta')\n",
+    "cl=Classification(model,documents,labels,process_classif=True)\n",
+    "print(cl.predict(documents[:2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tf_gpu",
+   "language": "python",
+   "name": "tf_gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}