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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 +1,384 @@
+import numpy as np
+import pandas as pd
+import re
+import os
+import cloudpickle
+from transformers import (DebertaTokenizerFast, 
+                          TFAutoModelForTokenClassification,
+                          BartTokenizerFast, 
+                          TFAutoModelForSeq2SeqLM)
+import tensorflow as tf
+import spacy
+import streamlit as st
+from scraper import scrape_text
+
+
+os.environ['TF_USE_LEGACY_KERAS'] = "1"
+
+class NERLabelEncoder:
+    '''
+    Label Encoder to encode and decode the entity labels
+    '''
+    def __init__(self):
+        self.label_mapping = {'O': 0, 
+                             'B-geo': 1, 
+                             'I-geo': 2, 
+                             'B-gpe': 3, 
+                             'I-gpe': 4, 
+                             'B-per': 5,
+                             'I-per': 6,
+                             'B-org': 7,
+                             'I-org': 8,
+                             'B-tim': 9,
+                             'I-tim': 10,
+                             'B-art': 11, 
+                             'I-art': 12,
+                             'B-nat': 13,
+                             'I-nat': 14,
+                             'B-eve': 15,
+                             'I-eve': 16,
+                             '[CLS]': -100,
+                             '[SEP]': -100}
+        
+        self.inverse_label_mapping = {}
+    
+    def fit(self):
+        self.inverse_label_mapping = {value: key for key, value in self.label_mapping.items()}
+        return self
+        
+    def transform(self, x: pd.Series):
+        x = x.map(self.label_mapping)
+        return x
+    
+    def inverse_transform(self, x: pd.Series):
+        x = x.map(self.inverse_label_mapping)
+        return x
+
+
+############ NER MODEL & VARS INITIALIZATION START ####################
+NER_CHECKPOINT = "microsoft/deberta-base"
+NER_N_TOKENS = 50
+NER_N_LABELS = 18
+NER_COLOR_MAP = {'GEO': '#DFFF00', 'GPE': '#FFBF00', 'PER': '#9FE2BF', 
+                 'ORG': '#40E0D0', 'TIM': '#CCCCFF', 'ART': '#FFC0CB', 'NAT': '#FFE4B5', 'EVE': '#DCDCDC'}
+
+@st.cache_resource
+def load_ner_models():
+    ner_model = TFAutoModelForTokenClassification.from_pretrained(NER_CHECKPOINT, num_labels=NER_N_LABELS, attention_probs_dropout_prob=0.4, hidden_dropout_prob=0.4)
+    ner_model.load_weights(os.path.join("models", "general_ner_deberta_weights.h5"), by_name=True)
+    ner_label_encoder = NERLabelEncoder()
+    ner_label_encoder.fit()
+    ner_tokenizer = DebertaTokenizerFast.from_pretrained(NER_CHECKPOINT, add_prefix_space=True)
+    nlp = spacy.load(os.path.join('.', 'en_core_web_sm-3.6.0'))
+    print('Loaded NER models')
+    return ner_model, ner_label_encoder, ner_tokenizer, nlp
+
+ner_model, ner_label_encoder, ner_tokenizer, nlp = load_ner_models()
+
+############ NER MODEL & VARS INITIALIZATION END ####################
+
+############ NER LOGIC START ####################
+def softmax(x):
+    return tf.exp(x) / tf.math.reduce_sum(tf.exp(x))
+
+def ner_process_output(res):
+    '''
+    Function to concatenate sub-word tokens, labels and 
+    compute mean prediction probability of tokens
+    '''
+    d = {}
+    result = []
+    pred_prob = []
+    res.append(['-', 'B-b', 0])
+    for n, i in enumerate(res):
+        try:
+            split = i[1].split('-')
+            token = i[0]
+            token_prob = i[2]
+            prefix, suffix = split
+            if prefix == 'B':
+                if len(d) != 0:
+                    result.append([(re.sub(r"[^\x00-\x7F]+", '', token.replace("Ġ", " ").strip()), label, np.mean(pred_prob))
+                                   for label, token in d.items()][0])
+                d = {}
+                pred_prob = []
+                pred_prob.append(token_prob)
+                d[suffix] = token
+
+            else:
+                d[suffix] = d[suffix] + token
+                pred_prob.append(token_prob)
+        except:
+            continue
+            
+    return result
+
+
+def ner_inference(txt):
+    '''
+    Function that returns model prediction and prediction probabitliy
+    '''
+    test_data = [txt]
+    # tokenizer = DebertaTokenizerFast.from_pretrained(NER_CHECKPOINT, add_prefix_space=True)
+    tokens = ner_tokenizer.tokenize(txt)
+    tokenized_data = ner_tokenizer(test_data, is_split_into_words=True, max_length=NER_N_TOKENS, 
+                               truncation=True, padding="max_length")
+
+    token_idx_to_consider = tokenized_data.word_ids()
+    token_idx_to_consider = [i for i in range(len(token_idx_to_consider)) if token_idx_to_consider[i] is not None] 
+
+    input_ = [tokenized_data['input_ids'], tokenized_data['attention_mask']]
+    pred_logits = ner_model.predict(input_, verbose=0).logits[0]
+
+    pred_prob = tf.map_fn(softmax, pred_logits)
+
+    pred_idx = tf.argmax(pred_prob, axis=-1).numpy()
+    pred_idx = pred_idx[token_idx_to_consider]
+
+    pred_prob = tf.math.reduce_max(pred_prob, axis=-1).numpy()
+    pred_prob = np.round(pred_prob[token_idx_to_consider], 3)
+    pred_labels = ner_label_encoder.inverse_transform(pd.Series(pred_idx))
+
+    result = [[token, label, prob] for token, label, 
+              prob in zip(tokens, pred_labels, pred_prob) if label.find('-') >= 0]
+    
+    output = ner_process_output(result)
+    return output
+
+
+def ner_inference_long_text(txt):
+    entities = []
+    doc = nlp(txt)
+    for sent in doc.sents:
+        entities.extend(ner_inference(sent.text))
+    return entities
+
+
+def get_ner_text(article_txt, ner_result):
+    res_txt = ''
+    start = 0
+    prev_start = 0
+    for i in ner_result:
+        try:
+            span = next(re.finditer(fr'{i[0]}', article_txt)).span()
+            start = span[0]
+            end = span[1]
+            res_txt += article_txt[prev_start:start]
+            repl_str = f'''<span style="background-color:{NER_COLOR_MAP[i[1]]}; border-radius: 3px">{article_txt[start:end].strip()}
+            <span style="font-size:10px; font-weight:bold; display:inline-block; vertical-align: middle;">
+            {i[1]} ({str(np.round(i[2], 3))})</span></span>'''
+            res_txt += article_txt[start:end].replace(article_txt[start:end], repl_str)
+            prev_start = 0
+            article_txt = article_txt[end:]
+        except:
+            continue
+    res_txt += article_txt
+    return res_txt
+
+############ NER LOGIC END ####################
+
+
+############ SUMMARIZATION MODEL & VARS INITIALIZATION START ####################
+SUMM_CHECKPOINT = "facebook/bart-base"
+SUMM_INPUT_N_TOKENS = 400
+SUMM_TARGET_N_TOKENS = 300
+
+@st.cache_resource
+def load_summarizer_models():
+    summ_tokenizer = BartTokenizerFast.from_pretrained(SUMM_CHECKPOINT)
+    summ_model = TFAutoModelForSeq2SeqLM.from_pretrained(SUMM_CHECKPOINT)
+    summ_model.load_weights(os.path.join("models", "bart_en_summarizer.h5"), by_name=True)
+    print('Loaded summarizer models')
+    return summ_tokenizer, summ_model
+
+summ_tokenizer, summ_model = load_summarizer_models()
+
+def summ_preprocess(txt):
+    txt = re.sub(r'^By \. [\w\s]+ \. ', ' ', txt) # By . Ellie Zolfagharifard . 
+    txt = re.sub(r'\d{1,2}\:\d\d [a-zA-Z]{3}', ' ', txt) # 10:30 EST
+    txt = re.sub(r'\d{1,2} [a-zA-Z]+ \d{4}', ' ', txt) # 10 November 1990
+    txt = txt.replace('PUBLISHED:', ' ')
+    txt = txt.replace('UPDATED', ' ')
+    txt = re.sub(r' [\,\.\:\'\;\|] ', ' ', txt) # remove puncts with spaces before and after
+    txt = txt.replace(' : ', ' ')
+    txt = txt.replace('(CNN)', ' ')
+    txt = txt.replace('--', ' ')
+    txt = re.sub(r'^\s*[\,\.\:\'\;\|]', ' ', txt) # remove puncts at beginning of sent
+    txt = re.sub(r' [\,\.\:\'\;\|] ', ' ', txt) # remove puncts with spaces before and after
+    txt = re.sub(r'\n+',' ', txt)
+    txt = " ".join(txt.split())
+    return txt
+
+def summ_inference_tokenize(input_: list, n_tokens: int):
+    tokenized_data = summ_tokenizer(text=input_, max_length=SUMM_TARGET_N_TOKENS, truncation=True, padding="max_length", return_tensors="tf")
+    return summ_tokenizer, tokenized_data    
+
+def summ_inference(txt: str):
+    txt = summ_preprocess(txt)
+    test_data = [txt]
+    inference_tokenizer, tokenized_data = summ_inference_tokenize(input_=test_data, n_tokens=SUMM_INPUT_N_TOKENS)
+    pred = summ_model.generate(**tokenized_data, max_new_tokens=SUMM_TARGET_N_TOKENS)
+    result = inference_tokenizer.decode(pred[0])
+    result = re.sub("<.*?>", "", result).strip()
+    return result
+############ SUMMARIZATION MODEL & VARS INITIALIZATION END ####################
+
+############## ENTRY POINT START #######################
+def main():
+    st.markdown('''<h3>News Summarizer and NER</h3>''', unsafe_allow_html=True)
+    input_type = st.radio('Select an option:', ['Paste news URL', 'Paste news text'], 
+                      horizontal=True)
+
+    scrape_error = None
+    summary_error = None
+    ner_error = None
+    summ_result = None
+    ner_result = None
+    ner_df = None
+    article_txt = None
+    
+    
+    if input_type == 'Paste news URL':
+        article_url = st.text_input("Paste the URL of a news article", "")
+        
+        if (st.button("Submit")) or (article_url):
+            with st.status("Processing...", expanded=True) as status:
+                status.empty()
+                # Scraping data Start
+                try:
+                    st.info("Scraping data from the URL.", icon="ℹ️")
+                    article_txt = scrape_text(article_url)
+                    st.success("Successfully scraped the data.", icon="✅")
+                except Exception as e:
+                    article_txt = None
+                    scrape_error = str(e) 
+
+                # Scraping data End
+    
+                if article_txt is not None:
+                    article_txt = re.sub(r'\n+',' ', article_txt)
+
+                    # Generating summary start
+                    
+                    try:
+                        st.info("Generating the summary.", icon="ℹ️")
+                        summ_result = summ_inference(article_txt)
+                    except Exception as e:
+                        summ_result = None
+                        summary_error = str(e)
+                    if summ_result is not None:
+                        st.success("Successfully generated the summary.", icon="✅")
+                    else:
+                        st.error("Encountered an error while generating the summary.", icon="🚨")
+
+                    # Generating summary end
+
+                    
+                    # NER start
+                    try:
+                        st.info("Recognizing the entites.", icon="ℹ️")
+                        ner_result = [[ent, label.upper(), np.round(prob, 3)] 
+                                      for ent, label, prob in ner_inference_long_text(article_txt)]
+
+                        ner_df = pd.DataFrame(ner_result, columns=['entity', 'label', 'confidence'])
+
+                        ner_result = get_ner_text(article_txt, ner_result).replace('$', '\$')
+            
+                    except Exception as e:
+                        ner_result = None
+                        ner_error = str(e)
+                    if ner_result is not None:
+                        st.success("Successfully recognized the entites.", icon="✅")
+                    else:
+                        st.error("Encountered an error while recognizing the entites.", icon="🚨")
+
+                    # NER end                                 
+                else:
+                    st.error("Encountered an error while scraping the data.", icon="🚨")
+
+                if (scrape_error is None) and (summary_error is None) and (ner_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 summary_error is not None:
+                    st.error(f"Summary Error:  \n{summary_error}", icon="🚨")
+                else:
+                    st.markdown(f"<h4>SUMMARY:</h4>{summ_result}", unsafe_allow_html=True)
+                    
+                if ner_error is not None:
+                    st.error(f"NER Error  \n{ner_error}", icon="🚨")
+                else:
+                    st.markdown(f"<h4>ENTITIES:</h4>{ner_result}", unsafe_allow_html=True)
+                    # st.dataframe(ner_df, use_container_width=True)
+    
+                st.markdown(f"<h4>SCRAPED TEXT:</h4>{article_txt}", unsafe_allow_html=True)       
+
+    else:
+        article_txt = st.text_area("Paste the text of a news article", "", height=150)
+
+        if (st.button("Submit")) or (article_txt):
+            with st.status("Processing...", expanded=True) as status:
+                article_txt = re.sub(r'\n+',' ', article_txt)
+
+                # Generating summary start
+                
+                try:
+                    st.info("Generating the summary.", icon="ℹ️")
+                    summ_result = summ_inference(article_txt)
+                except Exception as e:
+                    summ_result = None
+                    summary_error = str(e)
+                if summ_result is not None:
+                    st.success("Successfully generated the summary.", icon="✅")
+                else:
+                    st.error("Encountered an error while generating the summary.", icon="🚨")
+
+                # Generating summary end
+
+                
+                # NER start
+                try:
+                    st.info("Recognizing the entites.", icon="ℹ️")
+                    ner_result = [[ent, label.upper(), np.round(prob, 3)] 
+                                  for ent, label, prob in ner_inference_long_text(article_txt)]
+
+                    ner_df = pd.DataFrame(ner_result, columns=['entity', 'label', 'confidence'])
+
+                    ner_result = get_ner_text(article_txt, ner_result).replace('$', '\$')
+        
+                except Exception as e:
+                    ner_result = None
+                    ner_error = str(e)
+                if ner_result is not None:
+                    st.success("Successfully recognized the entites.", icon="✅")
+                else:
+                    st.error("Encountered an error while recognizing the entites.", icon="🚨")
+
+                    # NER end                                 
+
+                if (summary_error is None) and (ner_error is None):
+                    status.update(label="Done", state="complete", expanded=False)
+                else:
+                    status.update(label="Error", state="error", expanded=False)
+
+            if summary_error is not None:
+                st.error(f"Summary Error:  \n{summary_error}", icon="🚨")
+            else:
+                st.markdown(f"<h4>SUMMARY:</h4>{summ_result}", unsafe_allow_html=True)
+                
+            if ner_error is not None:
+                st.error(f"NER Error  \n{ner_error}", icon="🚨")
+            else:
+                st.markdown(f"<h4>ENTITIES:</h4>{ner_result}", unsafe_allow_html=True)
+                # st.dataframe(ner_df, use_container_width=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

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+{
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+  "speed": 8516.5316928954
+}

en_core_web_sm-3.6.0/config.cfg

@@ -0,0 +1,268 @@
+[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
+
+[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
+
+[components.ner.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[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
+
+[components.parser.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "parser"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.parser.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "tok2vec"
+
+[components.senter]
+factory = "senter"
+overwrite = false
+scorer = {"@scorers":"spacy.senter_scorer.v1"}
+
+[components.senter.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.senter.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.senter.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 16
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
+rows = [1000,500,500,500,50]
+include_static_vectors = false
+
+[components.senter.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 16
+depth = 2
+window_size = 1
+maxout_pieces = 2
+
+[components.tagger]
+factory = "tagger"
+label_smoothing = 0.0
+neg_prefix = "!"
+overwrite = false
+scorer = {"@scorers":"spacy.tagger_scorer.v1"}
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.tagger.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "tok2vec"
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode:width}
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY","IS_SPACE"]
+rows = [5000,1000,2500,2500,50,50]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+gold_preproc = false
+max_length = 0
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+gold_preproc = false
+max_length = 0
+limit = 0
+augmenter = null
+
+[training]
+train_corpus = "corpora.train"
+dev_corpus = "corpora.dev"
+seed = ${system:seed}
+gpu_allocator = ${system:gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 5000
+max_epochs = 0
+max_steps = 100000
+eval_frequency = 1000
+frozen_components = []
+before_to_disk = null
+annotating_components = []
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
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+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 100
+stop = 1000
+compound = 1.001
+t = 0.0
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+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
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+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = true
+eps = 0.00000001
+learn_rate = 0.001
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+[training.score_weights]
+tag_acc = 0.16
+dep_uas = 0.0
+dep_las = 0.16
+dep_las_per_type = null
+sents_p = null
+sents_r = null
+sents_f = 0.02
+lemma_acc = 0.5
+ents_f = 0.16
+ents_p = 0.0
+ents_r = 0.0
+ents_per_type = null
+speed = 0.0
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+[pretraining]
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+[initialize]
+vocab_data = null
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+before_init = null
+after_init = null
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+[initialize.components]
+
+[initialize.components.ner]
+
+[initialize.components.ner.labels]
+@readers = "spacy.read_labels.v1"
+path = "corpus/labels/ner.json"
+require = false
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+[initialize.components.parser]
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+[initialize.components.parser.labels]
+@readers = "spacy.read_labels.v1"
+path = "corpus/labels/parser.json"
+require = false
+
+[initialize.components.tagger]
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+[initialize.components.tagger.labels]
+@readers = "spacy.read_labels.v1"
+path = "corpus/labels/tagger.json"
+require = false
+
+[initialize.lookups]
+@misc = "spacy.LookupsDataLoader.v1"
+lang = ${nlp.lang}
+tables = ["lexeme_norm"]
+
+[initialize.tokenizer]

en_core_web_sm-3.6.0/lemmatizer/lookups/lookups.bin

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+oid sha256:eb64f40c0f8396d1762730c0ddf4dad2a52d138f5a389f71a1a1d088173b7737
+size 972893

en_core_web_sm-3.6.0/meta.json

@@ -0,0 +1,521 @@
+{
+  "lang":"en",
+  "name":"core_web_sm",
+  "version":"3.6.0",
+  "description":"English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.",
+  "author":"Explosion",
+  "email":"contact@explosion.ai",
+  "url":"https://explosion.ai",
+  "license":"MIT",
+  "spacy_version":">=3.6.0,<3.7.0",
+  "spacy_git_version":"cb4fdc83e",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null
+  },
+  "labels":{
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+    "tagger":[
+      "$",
+      "''",
+      ",",
+      "-LRB-",
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+      ".",
+      ":",
+      "ADD",
+      "AFX",
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+      "JJ",
+      "JJR",
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+      "NFP",
+      "NN",
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+      "NNPS",
+      "NNS",
+      "PDT",
+      "POS",
+      "PRP",
+      "PRP$",
+      "RB",
+      "RBR",
+      "RBS",
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+      "SYM",
+      "TO",
+      "UH",
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+      "WDT",
+      "WP",
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+      "WRB",
+      "XX",
+      "_SP",
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+    "parser":[
+      "ROOT",
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+      "acomp",
+      "advcl",
+      "advmod",
+      "agent",
+      "amod",
+      "appos",
+      "attr",
+      "aux",
+      "auxpass",
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+      "ccomp",
+      "compound",
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+      "csubjpass",
+      "dative",
+      "dep",
+      "det",
+      "dobj",
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+      "intj",
+      "mark",
+      "meta",
+      "neg",
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+      "nummod",
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+      "prep",
+      "prt",
+      "punct",
+      "quantmod",
+      "relcl",
+      "xcomp"
+    ],
+    "attribute_ruler":[
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+    ],
+    "lemmatizer":[
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+    ],
+    "ner":[
+      "CARDINAL",
+      "DATE",
+      "EVENT",
+      "FAC",
+      "GPE",
+      "LANGUAGE",
+      "LAW",
+      "LOC",
+      "MONEY",
+      "NORP",
+      "ORDINAL",
+      "ORG",
+      "PERCENT",
+      "PERSON",
+      "PRODUCT",
+      "QUANTITY",
+      "TIME",
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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_summarizer.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e6381d18af41ddc2b674cde800281c5eb65ece6f0c964ab5a0e5f20b362d801
+size 558172300

models/general_ner_deberta_weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5f3678669b14bb127bb716427cd883bef8ea29dfa011759987da79d3e130f77e
+size 554748296

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