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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+en_core_web_sm-3.6.0/ner/model filter=lfs diff=lfs merge=lfs -text
+en_core_web_sm-3.6.0/tok2vec/model filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,30 @@
+FROM python:3.10-slim
+WORKDIR /code
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+RUN apt update && apt install -y ffmpeg
+RUN apt -y install wget
+RUN apt -y install firefox-esr
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH \
+    GECKODRIVERURL=https://github.com/mozilla/geckodriver/releases/download/v0.34.0/geckodriver-v0.34.0-linux64.tar.gz \
+    GECKODRIVERFILENAME=geckodriver-v0.34.0-linux64.tar.gz
+    
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+RUN wget -P $HOME/app $GECKODRIVERURL
+RUN tar --warning=no-file-changed -xzf $HOME/app/$GECKODRIVERFILENAME
+RUN rm $HOME/app/$GECKODRIVERFILENAME
+
+RUN chmod -x geckodriver
+
+RUN ls -ltr
+
+EXPOSE 7860
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,175 @@
+import numpy as np
+import pandas as pd
+import re
+import os
+import cloudpickle
+from transformers import BartTokenizerFast, TFAutoModelForSeq2SeqLM
+import tensorflow as tf
+import spacy
+import streamlit as st
+from scraper import scrape_text
+
+# Training data https://www.kaggle.com/datasets/vladimirvorobevv/chatgpt-paraphrases
+
+os.environ['TF_USE_LEGACY_KERAS'] = "1"
+
+CHECKPOINT = "facebook/bart-base"
+INPUT_N_TOKENS = 70
+TARGET_N_TOKENS = 70
+
+
+@st.cache_resource
+def load_models():
+    nlp = spacy.load(os.path.join('.', 'en_core_web_sm-3.6.0'))
+    tokenizer = BartTokenizerFast.from_pretrained(CHECKPOINT)
+    model = TFAutoModelForSeq2SeqLM.from_pretrained(CHECKPOINT)
+    model.load_weights(os.path.join("models", "bart_en_paraphraser.h5"), by_name=True)
+    print('Loaded models')
+    return nlp, tokenizer, model
+
+nlp, tokenizer, model = load_models()
+
+def inference_tokenize(input_: list, n_tokens: int):
+    tokenized_data = tokenizer(text=input_, max_length=n_tokens, truncation=True, padding="max_length", return_tensors="tf")
+    return tokenizer, tokenized_data    
+
+def inference(txt: str):
+    try:
+        test_data = [txt]
+        inference_tokenizer, tokenized_data = inference_tokenize(input_=test_data, n_tokens=INPUT_N_TOKENS)
+        pred = model.generate(**tokenized_data, max_new_tokens=TARGET_N_TOKENS)
+        result = inference_tokenizer.decode(pred[0])
+        result = result.split('", "')
+        result = result if isinstance(result, str) else result[0]
+        result = re.sub('(<.*?>)', "", result).strip()
+        result = re.sub('^\"', "", result).strip()
+        return result
+    except:
+        return ""
+
+def inference_long_text(txt, n_sents):
+    paraphrased_txt = []
+    input_txt = []
+    doc = nlp(txt)
+    n_sents_in_doc = len([sent for sent in doc.sents])
+    n_sents = n_sents_in_doc if n_sents_in_doc < n_sents else n_sents
+    n = 0
+    progress_bar = st.progress(0.0)
+    for sent in doc.sents:
+        if n >= n_sents:
+            break
+        paraphrased_txt.append(inference(sent.text))
+        input_txt.append(sent.text)
+        n += 1
+        percent_complete = n / n_sents
+        progress_bar.progress(percent_complete, text=f'Processed {n} / {n_sents} sentences')
+    return input_txt, paraphrased_txt
+
+    
+############## ENTRY POINT START #######################
+def main():
+    st.markdown('''<h3>Text Rewriter</h3>''', unsafe_allow_html=True)
+    input_type = st.radio('Select an option:', ['Paste URL of the text', 'Paste the text'], 
+                      horizontal=True)
+
+    n_sents = st.slider('Select the number of sentences to process', 5, 20, 10)
+
+    scrape_error = None
+    paraphrase_error = None
+    paraphrased_txt = None
+    input_txt = None
+    
+    
+    if input_type == 'Paste URL of the text':
+        input_url = st.text_input("Paste URL of the text", "")
+        
+        if (st.button("Submit")) or (input_url):
+            with st.status("Processing...", expanded=True) as status:
+                status.empty()
+                # Scraping data Start
+                try:
+                    st.info("Scraping data from the URL.", icon="ℹ️")
+                    input_txt = scrape_text(input_url)
+                    st.success("Successfully scraped the data.", icon="✅")
+                except Exception as e:
+                    input_txt = None
+                    scrape_error = str(e) 
+
+                # Scraping data End
+    
+                if input_txt is not None:
+                    input_txt = re.sub(r'\n+',' ', input_txt)
+
+                    # Paraphrasing start
+                    
+                    try:
+                        st.info("Rewriting the text. This takes time.", icon="ℹ️")
+                        input_txt, paraphrased_txt = inference_long_text(input_txt, n_sents)
+                        input_txt = "<br><br>".join(input_txt)
+                        paraphrased_txt = "<br><br>".join(paraphrased_txt)
+                    except Exception as e:
+                        paraphrased_txt = None
+                        paraphrase_error = str(e)
+                    if paraphrased_txt is not None:
+                        st.success("Successfully rewrote the text.", icon="✅")
+                    else:
+                        st.error("Encountered an error while rewriting the text.", icon="🚨")
+
+                    # Paraphrasing end
+
+                else:
+                    st.error("Encountered an error while scraping the data.", icon="🚨")
+
+                if (scrape_error is None) and (paraphrase_error is None):
+                    status.update(label="Done", state="complete", expanded=False)
+                else:
+                    status.update(label="Error", state="error", expanded=False)
+
+            if scrape_error is not None:
+                st.error(f"Scrape Error:  \n{scrape_error}", icon="🚨")
+            else:
+                if paraphrase_error is not None:
+                    st.error(f"Paraphrasing Error:  \n{paraphrase_error}", icon="🚨")
+                else:
+                    st.markdown(f"<h3>Scraped Text ({n_sents} sentences)</h3><p>{input_txt}</p><br><h3>Rewritten Text ({n_sents} sentences)</h3><p>{paraphrased_txt}</p>", unsafe_allow_html=True)
+
+    else:
+        input_txt = st.text_area("Enter the text. (Ensure the text is grammatically correct and has punctuations at the right places):", "", height=150)
+
+        if (st.button("Submit")) or (input_txt):
+            with st.status("Processing...", expanded=True) as status:
+                input_txt = re.sub(r'\n+',' ', input_txt)
+
+                # Paraphrasing start
+                
+                try:
+                    st.info("Rewriting the text. This takes time.", icon="ℹ️")
+                    input_txt, paraphrased_txt = inference_long_text(input_txt, n_sents)
+                    input_txt = "<br><br>".join(input_txt)
+                    paraphrased_txt = "<br><br>".join(paraphrased_txt)
+                except Exception as e:
+                    paraphrased_txt = None
+                    paraphrase_error = str(e)
+                if paraphrased_txt is not None:
+                    st.success("Successfully rewrote the text.", icon="✅")
+                else:
+                    st.error("Encountered an error while rewriting the text.", icon="🚨")
+
+                # Paraphrasing end
+
+                if paraphrase_error is None:
+                    status.update(label="Done", state="complete", expanded=False)
+                else:
+                    status.update(label="Error", state="error", expanded=False)
+
+            if paraphrase_error is not None:
+                st.error(f"Paraphrasing Error:  \n{paraphrase_error}", icon="🚨")
+            else:
+                st.markdown(f"<h3>Scraped Text ({n_sents} sentences)</h3><p>{input_txt}</p><br><h3>Rewritten Text ({n_sents} sentences)</h3><p>{paraphrased_txt}</p>", unsafe_allow_html=True)
+        
+        
+
+############## ENTRY POINT END #######################
+
+if __name__ == "__main__":
+    main()

en_core_web_sm-3.6.0/LICENSE

@@ -0,0 +1,19 @@
+Copyright 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

en_core_web_sm-3.6.0/LICENSES_SOURCES

@@ -0,0 +1,66 @@
+# OntoNotes 5
+
+* Author: Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston
+* URL: https://catalog.ldc.upenn.edu/LDC2013T19
+* License: commercial (licensed by Explosion)
+
+```
+```
+
+
+
+
+# ClearNLP Constituent-to-Dependency Conversion
+
+* Author: Emory University
+* URL: https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md
+* License: Citation provided for reference, no code packaged with model
+
+```
+```
+
+
+
+
+# WordNet 3.0
+
+* Author: Princeton University
+* URL: https://wordnet.princeton.edu/
+* License: WordNet 3.0 License
+
+```
+WordNet Release 3.0
+
+This software and database is being provided to you, the LICENSEE, by  
+Princeton University under the following license.  By obtaining, using  
+and/or copying this software and database, you agree that you have  
+read, understood, and will comply with these terms and conditions.:  
+  
+Permission to use, copy, modify and distribute this software and  
+database and its documentation for any purpose and without fee or  
+royalty is hereby granted, provided that you agree to comply with  
+the following copyright notice and statements, including the disclaimer,  
+and that the same appear on ALL copies of the software, database and  
+documentation, including modifications that you make for internal  
+use or for distribution.  
+  
+WordNet 3.0 Copyright 2006 by Princeton University.  All rights reserved.  
+  
+THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON  
+UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR  
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON  
+UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT-  
+ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE  
+OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT  
+INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR  
+OTHER RIGHTS.  
+  
+The name of Princeton University or Princeton may not be used in  
+advertising or publicity pertaining to distribution of the software  
+and/or database.  Title to copyright in this software, database and  
+any associated documentation shall at all times remain with  
+Princeton University and LICENSEE agrees to preserve same.```
+
+
+
+

en_core_web_sm-3.6.0/README.md

@@ -0,0 +1,47 @@
+### Details: https://spacy.io/models/en#en_core_web_sm
+
+English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
+
+| Feature | Description |
+| --- | --- |
+| **Name** | `en_core_web_sm` |
+| **Version** | `3.6.0` |
+| **spaCy** | `>=3.6.0,<3.7.0` |
+| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
+| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
+| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
+| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
+| **License** | `MIT` |
+| **Author** | [Explosion](https://explosion.ai) |
+
+### Label Scheme
+
+<details>
+
+<summary>View label scheme (113 labels for 3 components)</summary>
+
+| Component | Labels |
+| --- | --- |
+| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
+| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
+| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
+
+</details>
+
+### Accuracy
+
+| Type | Score |
+| --- | --- |
+| `TOKEN_ACC` | 99.86 |
+| `TOKEN_P` | 99.57 |
+| `TOKEN_R` | 99.58 |
+| `TOKEN_F` | 99.57 |
+| `TAG_ACC` | 97.25 |
+| `SENTS_P` | 92.02 |
+| `SENTS_R` | 89.21 |
+| `SENTS_F` | 90.59 |
+| `DEP_UAS` | 91.75 |
+| `DEP_LAS` | 89.87 |
+| `ENTS_P` | 84.55 |
+| `ENTS_R` | 84.57 |
+| `ENTS_F` | 84.56 |

en_core_web_sm-3.6.0/accuracy.json

@@ -0,0 +1,330 @@
+{
+  "token_acc": 0.9986194413,
+  "token_p": 0.9956819193,
+  "token_r": 0.9957659295,
+  "token_f": 0.9957239226,
+  "tag_acc": 0.97246532,
+  "sents_p": 0.9201877934,
+  "sents_r": 0.8921432812,
+  "sents_f": 0.9059485531,
+  "dep_uas": 0.9175304332,
+  "dep_las": 0.89874821,
+  "dep_las_per_type": {
+    "prep": {
+      "p": 0.853521338,
+      "r": 0.8635932461,
+      "f": 0.8585277532
+    },
+    "det": {
+      "p": 0.9763930156,
+      "r": 0.9781048683,
+      "f": 0.9772481923
+    },
+    "pobj": {
+      "p": 0.9613764045,
+      "r": 0.967681131,
+      "f": 0.9645184649
+    },
+    "nsubj": {
+      "p": 0.9565737052,
+      "r": 0.9467250821,
+      "f": 0.9516239128
+    },
+    "aux": {
+      "p": 0.9815061794,
+      "r": 0.9827294578,
+      "f": 0.9821174377
+    },
+    "advmod": {
+      "p": 0.8548033091,
+      "r": 0.8519266364,
+      "f": 0.8533625485
+    },
+    "relcl": {
+      "p": 0.7571736011,
+      "r": 0.7659651669,
+      "f": 0.7615440115
+    },
+    "root": {
+      "p": 0.9195942266,
+      "r": 0.8910218352,
+      "f": 0.9050825879
+    },
+    "xcomp": {
+      "p": 0.8836222144,
+      "r": 0.8966259871,
+      "f": 0.8900766079
+    },
+    "amod": {
+      "p": 0.9174389766,
+      "r": 0.9107223842,
+      "f": 0.9140683422
+    },
+    "compound": {
+      "p": 0.9126489559,
+      "r": 0.9298284696,
+      "f": 0.9211586207
+    },
+    "poss": {
+      "p": 0.9739583333,
+      "r": 0.9786634461,
+      "f": 0.9763052209
+    },
+    "ccomp": {
+      "p": 0.7671207315,
+      "r": 0.8372708758,
+      "f": 0.8006621872
+    },
+    "attr": {
+      "p": 0.899837794,
+      "r": 0.93313709,
+      "f": 0.9161849711
+    },
+    "case": {
+      "p": 0.9787549407,
+      "r": 0.9914914915,
+      "f": 0.9850820487
+    },
+    "mark": {
+      "p": 0.9068783069,
+      "r": 0.9083200848,
+      "f": 0.9075986232
+    },
+    "intj": {
+      "p": 0.6717131474,
+      "r": 0.6175824176,
+      "f": 0.6435114504
+    },
+    "advcl": {
+      "p": 0.6633986928,
+      "r": 0.6645681189,
+      "f": 0.6639828909
+    },
+    "cc": {
+      "p": 0.8323511726,
+      "r": 0.8277717976,
+      "f": 0.8300551691
+    },
+    "neg": {
+      "p": 0.9466865969,
+      "r": 0.9533366784,
+      "f": 0.95
+    },
+    "conj": {
+      "p": 0.7567333828,
+      "r": 0.7710221551,
+      "f": 0.763810949
+    },
+    "nsubjpass": {
+      "p": 0.9182939363,
+      "r": 0.9164102564,
+      "f": 0.9173511294
+    },
+    "auxpass": {
+      "p": 0.9501335708,
+      "r": 0.9722095672,
+      "f": 0.9610448097
+    },
+    "dobj": {
+      "p": 0.9229805886,
+      "r": 0.9396764682,
+      "f": 0.9312537019
+    },
+    "nummod": {
+      "p": 0.9379292801,
+      "r": 0.9310606061,
+      "f": 0.9344823216
+    },
+    "npadvmod": {
+      "p": 0.7629658087,
+      "r": 0.7055062167,
+      "f": 0.7331118494
+    },
+    "prt": {
+      "p": 0.8118323747,
+      "r": 0.8853046595,
+      "f": 0.8469781397
+    },
+    "pcomp": {
+      "p": 0.8835714286,
+      "r": 0.8662464986,
+      "f": 0.8748231966
+    },
+    "expl": {
+      "p": 0.9851380042,
+      "r": 0.9935760171,
+      "f": 0.9893390192
+    },
+    "acl": {
+      "p": 0.742010459,
+      "r": 0.6966721222,
+      "f": 0.7186268993
+    },
+    "agent": {
+      "p": 0.9034482759,
+      "r": 0.9390681004,
+      "f": 0.920913884
+    },
+    "dative": {
+      "p": 0.8,
+      "r": 0.6972477064,
+      "f": 0.7450980392
+    },
+    "acomp": {
+      "p": 0.9020594966,
+      "r": 0.893877551,
+      "f": 0.8979498861
+    },
+    "dep": {
+      "p": 0.4147286822,
+      "r": 0.1737012987,
+      "f": 0.2448512586
+    },
+    "csubj": {
+      "p": 0.6983240223,
+      "r": 0.7396449704,
+      "f": 0.7183908046
+    },
+    "quantmod": {
+      "p": 0.8727436823,
+      "r": 0.7855402112,
+      "f": 0.8268490808
+    },
+    "nmod": {
+      "p": 0.7498033045,
+      "r": 0.5807434491,
+      "f": 0.654532967
+    },
+    "appos": {
+      "p": 0.7048498845,
+      "r": 0.6620390456,
+      "f": 0.6827740492
+    },
+    "predet": {
+      "p": 0.8299595142,
+      "r": 0.8798283262,
+      "f": 0.8541666667
+    },
+    "preconj": {
+      "p": 0.5544554455,
+      "r": 0.6511627907,
+      "f": 0.5989304813
+    },
+    "oprd": {
+      "p": 0.8013245033,
+      "r": 0.7223880597,
+      "f": 0.759811617
+    },
+    "parataxis": {
+      "p": 0.6428571429,
+      "r": 0.4880694143,
+      "f": 0.5548705302
+    },
+    "meta": {
+      "p": 0.3770491803,
+      "r": 0.4423076923,
+      "f": 0.407079646
+    },
+    "csubjpass": {
+      "p": 0.5555555556,
+      "r": 0.8333333333,
+      "f": 0.6666666667
+    }
+  },
+  "ents_p": 0.8454836771,
+  "ents_r": 0.8456530449,
+  "ents_f": 0.8455683525,
+  "ents_per_type": {
+    "DATE": {
+      "p": 0.8603213844,
+      "r": 0.8838095238,
+      "f": 0.8719072972
+    },
+    "GPE": {
+      "p": 0.9146932953,
+      "r": 0.8942817294,
+      "f": 0.9043723554
+    },
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+}

en_core_web_sm-3.6.0/config.cfg

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+[paths]
+train = null
+dev = null
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = null
+seed = 0
+
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","tagger","parser","senter","attribute_ruler","lemmatizer","ner"]
+disabled = ["senter"]
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+batch_size = 256
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.attribute_ruler]
+factory = "attribute_ruler"
+scorer = {"@scorers":"spacy.attribute_ruler_scorer.v1"}
+validate = false
+
+[components.lemmatizer]
+factory = "lemmatizer"
+mode = "rule"
+model = null
+overwrite = false
+scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
+
+[components.ner]
+factory = "ner"
+incorrect_spans_key = null
+moves = null
+scorer = {"@scorers":"spacy.ner_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.ner.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
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+[components.ner.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.ner.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 96
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,1000,2500,2500]
+include_static_vectors = false
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+[components.ner.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
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+[components.parser]
+factory = "parser"
+learn_tokens = false
+min_action_freq = 30
+moves = null
+scorer = {"@scorers":"spacy.parser_scorer.v1"}
+update_with_oracle_cut_size = 100
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+state_type = "parser"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
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+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "tok2vec"
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+[components.senter]
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+overwrite = false
+scorer = {"@scorers":"spacy.senter_scorer.v1"}
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+[components.senter.model]
+@architectures = "spacy.Tagger.v2"
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+width = 16
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
+rows = [1000,500,500,500,50]
+include_static_vectors = false
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+[components.senter.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 16
+depth = 2
+window_size = 1
+maxout_pieces = 2
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+[components.tagger]
+factory = "tagger"
+label_smoothing = 0.0
+neg_prefix = "!"
+overwrite = false
+scorer = {"@scorers":"spacy.tagger_scorer.v1"}
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+nO = null
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+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "tok2vec"
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+factory = "tok2vec"
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+[components.tok2vec.model]
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+attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY","IS_SPACE"]
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+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
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+[corpora]
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+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
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+path = "corpus/labels/tagger.json"
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+[initialize.lookups]
+@misc = "spacy.LookupsDataLoader.v1"
+lang = ${nlp.lang}
+tables = ["lexeme_norm"]
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+[initialize.tokenizer]

en_core_web_sm-3.6.0/lemmatizer/lookups/lookups.bin

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en_core_web_sm-3.6.0/meta.json

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+{
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+  "description":"English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.",
+  "author":"Explosion",
+  "email":"[email protected]",
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+      "author":"Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston"
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+      "license":"Citation provided for reference, no code packaged with model",
+      "author":"Emory University"
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en_core_web_sm-3.6.0/vocab/key2row

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+�

en_core_web_sm-3.6.0/vocab/lookups.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ddd140ecac6a8c4592e9146d6e30074569ffaed97ee51edc9587dc510f8934c
+size 69982

en_core_web_sm-3.6.0/vocab/vectors.cfg

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+{
+  "mode":"default"
+}

models/bart_en_paraphraser.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:37919a59ca2c320e10cc90622f03207d062c4fa2567488e8ef69a150f9aab4cd
+size 558172300

requirements.txt

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+numpy
+pandas
+cloudpickle
+transformers==4.39.3
+tensorflow==2.15.0
+spacy
+unidecode
+tf-keras==2.15.0
+selenium==4.19.0
+streamlit

scraper.py

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+from selenium import webdriver
+from selenium.webdriver.common.by import By
+import streamlit as st
+from selenium.webdriver import FirefoxOptions
+import re
+import logging
+
+
+
+def scrape_text(url, n_words=15):
+    try:
+        driver = None
+        logging.warning("Initiated Scraping")
+        opts = FirefoxOptions()
+        opts.add_argument("--headless")
+        driver = webdriver.Firefox(options=opts)
+        driver.set_page_load_timeout(60)
+        driver.get(url)
+        elem = driver.find_element(By.TAG_NAME, "body").text
+        # h1 = driver.find_element(By.TAG_NAME, "h1").text
+        # elem = h1 + "\n" + elem
+        sents = elem.split("\n")
+        sentence_list = []
+        for sent in sents:
+            sent = sent.strip()
+            if (len(sent.split()) >= n_words) and (len(re.findall(r"^\w.+[^\w\)\s]$", sent))>0):
+                sentence_list.append(sent)
+        driver.quit()
+        logging.warning("Closed Webdriver")
+        logging.warning("Successfully scraped text")
+        return "  \n\n\n".join(sentence_list)
+    except:
+        if driver:
+            driver.close()
+            logging.warning("Closed Webdriver")
+        raise