Update README.md

README.md

@@ -8,6 +8,7 @@ tags:
   - ner
   - named-entity-recognition
 pipeline_tag: token-classification
+inference: false
 language:
   - fa
 ---
@@ -15,40 +16,39 @@ language:
 
 # Hengam: An Adversarially Trained Transformer for Persian Temporal Tagging
 
-## HuggingFace
-- [HengamTrans Space](https://huggingface.co/spaces/kargaranamir/Hengam)
-- [HengamTagger (Parstdex) Space](https://huggingface.co/spaces/kargaranamir/parstdex)
-- [HengamTrans Models (ModelCard)](https://huggingface.co/kargaranamir/Hengam)
-- [HengamCorpus (Dataset)](https://huggingface.co/datasets/kargaranamir/HengamCorpus)
+# Usage
 
+You can use this model directly downloading the utils and requirements files and installing requirements:
 
-## Code (Software)
-
-### HengamTagger
-The HengamTagger is distributed as [Parstdex](https://github.com/kargaranamir/parstdex) package (will be continuously updated) and is available via [pip](https://pypi.org/project/parstdex). 
-
-### HengamTransformer
-In this directory 4 different notebooks are provided. Click on the hyperlinks to open each in Google Colab.
+```python
+! wget https://huggingface.co/spaces/kargaranamir/Hengam/raw/main/utils.py
+! wget https://huggingface.co/spaces/kargaranamir/Hengam/raw/main/requirements.txt
+! pip install -r requirements.txt
+```
 
-- Inference and Test 
-  - [Inference_HengamTransW.ipynb](https://colab.research.google.com/github/kargaranamir/hengam/blob/main/code/Inference_HengamTransW.ipynb): In this notebook, [HengamTransW.pth](https://huggingface.co/kargaranamir/Hengam/resolve/main/HengamTransW.pth) is downloaded from [Hengam HuggingFace model card](https://huggingface.co/kargaranamir/Hengam), and an inference is provided in the last cells.
-  - [Inference_HengamTransA.ipynb](https://colab.research.google.com/github/kargaranamir/hengam/blob/main/code/Inference_HengamTransA.ipynb): In this notebook, [HengamTransA.pth](https://huggingface.co/kargaranamir/Hengam/resolve/main/HengamTransA.pth) is downloaded from [Hengam HuggingFace model card](https://huggingface.co/kargaranamir/Hengam), and an inference is provided in the last cells.
-- Train
-  - [Train_HengamTransW.ipynb](https://colab.research.google.com/github/kargaranamir/hengam/blob/main/code/Train_HengamTransW.ipynb): This notebook uses the [HengamCorpus](https://huggingface.co/datasets/kargaranamir/HengamCorpus) dataset uploaded on HuggingFace. Afterwards, the HengamTransW will be trained.
-  - [Train_HengamTransA.ipynb](https://colab.research.google.com/github/kargaranamir/hengam/blob/main/code/Train_HengamTransA.ipynb): This notebook uses the trained [HengamTransW.pth](https://huggingface.co/kargaranamir/Hengam/resolve/main/HengamTransW.pth) downloaded from HuggingFace and then trains it on [strong labeled data](https://huggingface.co/datasets/kargaranamir/HengamCorpus/raw/main/strong.txt) in an adverserial manner to produce [HengamTransA](https://huggingface.co/kargaranamir/Hengam/resolve/main/HengamTransA.pth).
+and downloading the models HengamTransA.pth or HengamTransW.pth and building ner pipline:
 
-## Data
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from utils import *
 
-### Train Data
-HengamCorpus data along with strong labeled data is uploaded in [HuggingFace](https://huggingface.co/datasets/kargaranamir/HengamCorpus). Click on hyperlinks to download.
-- HengamCorpus
-  - [HengamCorpus train data](https://huggingface.co/datasets/kargaranamir/HengamCorpus/resolve/main/train.txt)
-  - [HengamCorpus test data](https://huggingface.co/datasets/kargaranamir/HengamCorpus/resolve/main/test.txt)
-  - [HengamCorpus validation data](https://huggingface.co/datasets/kargaranamir/HengamCorpus/resolve/main/val.txt)
-- [Strong labeled data](https://huggingface.co/datasets/kargaranamir/HengamCorpus/raw/main/strong.txt)
+# HengamTransW = hf_hub_download(repo_id="kargaranamir/Hengam", filename="HengamTransW.pth")
+HengamTransA = hf_hub_download(repo_id="kargaranamir/Hengam", filename="HengamTransA.pth")
+```
 
-### Evaluation Data
-HengamGold, challenge set and prediction result of different models on HengamGold is available in [evaluation](./data/evaluation) folder.
+```python
+# ner = NER(model_path=HengamTransW, tags=['B-TIM', 'I-TIM', 'B-DAT', 'I-DAT', 'O'])
+ner = NER(model_path=HengamTransA, tags=['B-TIM', 'I-TIM', 'B-DAT', 'I-DAT', 'O'])
+ner('.سلام من و دوستم ساعت ۸ صبح روز سه شنبه رفتیم دوشنبه بازار ')
+>>
+[{'Text': 'ساعت', 'Tag': 'B-TIM', 'Start': 17, 'End': 21},
+ {'Text': '۸', 'Tag': 'I-TIM', 'Start': 22, 'End': 23},
+ {'Text': 'صبح', 'Tag': 'I-TIM', 'Start': 24, 'End': 27},
+ {'Text': 'روز', 'Tag': 'I-TIM', 'Start': 28, 'End': 31},
+ {'Text': 'سه', 'Tag': 'B-DAT', 'Start': 32, 'End': 34},
+ {'Text': 'شنبه', 'Tag': 'I-DAT', 'Start': 35, 'End': 39}]
+```
 
 
 ## Citation