From 7aa67b281e1351f1bbb80a6c1c579a3ceadfb8c8 Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Wed, 13 Jul 2022 15:56:44 +0200
Subject: [PATCH] update
---
Linker/Linker.py | 9 ++++++++-
README.md | 33 +++++++++++++++++++++++++++++++--
postprocessing.py | 2 +-
3 files changed, 40 insertions(+), 4 deletions(-)
diff --git a/Linker/Linker.py b/Linker/Linker.py
index 5b86fe8..76596f0 100644
--- a/Linker/Linker.py
+++ b/Linker/Linker.py
@@ -380,6 +380,9 @@ class Linker(Module):
Args :
sentence : list of words composing the sentence
categories : list of categories (tags) of each word
+
+ Return :
+ links : links prediction
"""
self.eval()
with torch.no_grad():
@@ -413,6 +416,10 @@ class Linker(Module):
Args :
sentence : list of words composing the sentence
+
+ Return :
+ categories : the supertags predicted
+ links : links prediction
"""
self.eval()
with torch.no_grad():
@@ -440,7 +447,7 @@ class Linker(Module):
logits_predictions = self(num_atoms_per_word, atoms_tokenized, pos_idx, neg_idx, output['word_embeding'])
axiom_links_pred = torch.argmax(logits_predictions, dim=3)
- return axiom_links_pred
+ return categories, axiom_links_pred
def load_weights(self, model_file):
print("#" * 15)
diff --git a/README.md b/README.md
index 97a9213..ce70e16 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,35 @@ Clone the project locally.
Run the init.sh script or install the Tagger project under SuperTagger name. And upload the tagger.pt in the directory 'models'. (You may need to modify 'model_tagger' in train.py.)
+### Structure
+
+The structure should look like this :
+```
+.
+.
+├── Configuration # Configuration
+│ ├── Configuration.py # Contains the function to execute for config
+│ └── config.ini # contains parameters
+├── find_config.py # auto-configurate datasets parameters (max length sentence etc) according to the dataset given
+├── requirements.txt # librairies needed
+├── Datasets # TLGbank data with links
+├── SuperTagger # The Supertagger directory (that you need to install)
+│ ├── Datasets # TLGbank data
+│ ├── SuperTagger # Implementation of BertForTokenClassification
+│ │ ├── SuperTagger.py # Main class
+│ │ └── Tagging_bert_model.py # Bert model
+│ ├── predict.py # Example of prediction for supertagger
+│ └── train.py # Example of train for supertagger
+├── Linker # The Linker directory
+│ ├── ...
+│ └── Linker.py # Linker class containing the neural network
+├── models
+│ └── supertagger.pt # the pt file contaning the pretrained supertagger (you need to install it)
+├── Output # Directory where your linker models will be savec if checkpoint=True in train
+├── TensorBoard # Directory where the stats will be savec if tensorboard=True in train
+└── train.py # Example of train
+```
+
### Dataset format
The sentences should be in a column "X", the links with '_x' postfix should be in a column "Y" and the categories in a column "Z".
@@ -24,8 +53,8 @@ For the links each atom_x goes with the one and only other atom_x in the sentenc
Launch train.py, if you look at it you can give another dataset file and another tagging model.
-In train, if you use `checkpoint=True`, the model is automatically saved in a folder: Training_XX-XX_XX-XX. It saves
-after each epoch. Use `tensorboard=True` for log in same folder. (`tensorboard --logdir=logs` for see logs)
+In train, if you use `checkpoint=True`, the model is automatically saved in a folder: Output/Training_XX-XX_XX-XX. It saves
+after each epoch. Use `tensorboard=True` for log saving in folder TensorBoard. (`tensorboard --logdir=logs` for see logs)
## Predicting
diff --git a/postprocessing.py b/postprocessing.py
index d2d43f0..ff7fbfb 100644
--- a/postprocessing.py
+++ b/postprocessing.py
@@ -77,7 +77,7 @@ def draw_sentence_output(sentence, categories, links):
Drawing the prediction of a sentence when given categories and links predictions
:param sentence: list of words
:param categories: list of categories
- :param links: links predicted
+ :param links: links predicted, output of predict_with/without_categories
:return: dot source
"""
dot = graphviz.Graph('linking', comment='Axiom linking')
--
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