tags

README.md

@@ -8,135 +8,146 @@ tags:
 - instruct
 - instructions
 - code
+- instructiongen
 metrics:
 - rouge
 language:
 - en
 widget:
-  - text: |
-      git lfs install
-      huggingface-cli lfs-enable-largefiles .
-      git lfs track "*.bin"
-      git add .
-      git commit -a -m "add fp32 chkpt"
-      git push
-    example_title: bash
-
-  - text: |
-      export interface DocumentParams {
-        pageContent: string;
-
-        // eslint-disable-next-line @typescript-eslint/no-explicit-any
-        metadata: Record<string, any>;
+- text: |
+    git lfs install
+    huggingface-cli lfs-enable-largefiles .
+    git lfs track "*.bin"
+    git add .
+    git commit -a -m "add fp32 chkpt"
+    git push
+  example_title: bash
+- text: |
+    export interface DocumentParams {
+      pageContent: string;
+
+      // eslint-disable-next-line @typescript-eslint/no-explicit-any
+      metadata: Record<string, any>;
+    }
+
+    /**
+     * Interface for interacting with a document.
+     */
+    export class Document implements DocumentParams {
+      pageContent: string;
+
+      // eslint-disable-next-line @typescript-eslint/no-explicit-any
+      metadata: Record<string, any>;
+
+      constructor(fields?: Partial<DocumentParams>) {
+        this.pageContent = fields?.pageContent ?? this.pageContent;
+        this.metadata = fields?.metadata ?? {};
       }
+    }
+  example_title: js
+- text: |
+    def merge(left, right):
+        if len(left) == 0:
+            return right
 
-      /**
-       * Interface for interacting with a document.
-       */
-      export class Document implements DocumentParams {
-        pageContent: string;
+        if len(right) == 0:
+            return left
 
-        // eslint-disable-next-line @typescript-eslint/no-explicit-any
-        metadata: Record<string, any>;
+        result = []
+        index_left = index_right = 0
 
-        constructor(fields?: Partial<DocumentParams>) {
-          this.pageContent = fields?.pageContent ?? this.pageContent;
-          this.metadata = fields?.metadata ?? {};
-        }
-      }
-    example_title: js
-  - text: |
-      def merge(left, right):
-          if len(left) == 0:
-              return right
-
-          if len(right) == 0:
-              return left
-
-          result = []
-          index_left = index_right = 0
-
-          while len(result) < len(left) + len(right):
-              if left[index_left] <= right[index_right]:
-                  result.append(left[index_left])
-                  index_left += 1
-              else:
-                  result.append(right[index_right])
-                  index_right += 1
-
-              if index_right == len(right):
-                  result += left[index_left:]
-                  break
-
-              if index_left == len(left):
-                  result += right[index_right:]
-                  break
-
-          return result
-    example_title: merge
-
-  - text: |
-      import pandas as pd
-      import plotly.graph_objects as go
-
-      df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
-
-      fig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],
-                        name='Share Prices (in USD)'))
-
-      fig.update_layout(title='Apple Share Prices over time (2014)',
-                         plot_bgcolor='rgb(230, 230,230)',
-                         showlegend=True)
-
-      fig.show()
-    example_title: plot
-  - text: |
-      from spellchecker import SpellChecker
-
-      spell = SpellChecker()
-
-      def check_word_spelling(word: str):
-          misspelled = spell.unknown([word])
-          return len(misspelled) == 0
-
-      def eval_and_replace(text: str, match_token: str = "- "):
-          if match_token not in text:
-              return text
-          else:
-              while True:
-                  full_before_text = text.split(match_token, maxsplit=1)[0]
-                  before_text = [
-                      char for char in full_before_text.split()[-1] if char.isalpha()
-                  ]
-                  before_text = "".join(before_text)
-                  full_after_text = text.split(match_token, maxsplit=1)[-1]
-                  after_text = [char for char in full_after_text.split()[0] if char.isalpha()]
-                  after_text = "".join(after_text)
-                  full_text = before_text + after_text
-                  if check_word_spelling(full_text):
-                      text = full_before_text + full_after_text
-                  else:
-                      text = full_before_text + " " + full_after_text
-                  if match_token not in text:
-                      break
-              return text
-
-      text = "I- am- a go- od- boy"
-      eval_and_replace(text)
-    example_title: spell check
-  - text: |
-      import torch
-      from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-      checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
-      tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-      model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
-      sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]
-
-      tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
-      output = model(**tokens)
-    example_title: model inference
+        while len(result) < len(left) + len(right):
+            if left[index_left] <= right[index_right]:
+                result.append(left[index_left])
+                index_left += 1
+            else:
+                result.append(right[index_right])
+                index_right += 1
+
+            if index_right == len(right):
+                result += left[index_left:]
+                break
+
+            if index_left == len(left):
+                result += right[index_right:]
+                break
+
+        return result
+  example_title: merge
+- text: >
+    import pandas as pd
+
+    import plotly.graph_objects as go
+
+
+    df =
+    pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
+
+
+    fig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],
+                      name='Share Prices (in USD)'))
+
+    fig.update_layout(title='Apple Share Prices over time (2014)',
+                       plot_bgcolor='rgb(230, 230,230)',
+                       showlegend=True)
+
+    fig.show()
+  example_title: plot
+- text: |
+    from spellchecker import SpellChecker
+
+    spell = SpellChecker()
+
+    def check_word_spelling(word: str):
+        misspelled = spell.unknown([word])
+        return len(misspelled) == 0
+
+    def eval_and_replace(text: str, match_token: str = "- "):
+        if match_token not in text:
+            return text
+        else:
+            while True:
+                full_before_text = text.split(match_token, maxsplit=1)[0]
+                before_text = [
+                    char for char in full_before_text.split()[-1] if char.isalpha()
+                ]
+                before_text = "".join(before_text)
+                full_after_text = text.split(match_token, maxsplit=1)[-1]
+                after_text = [char for char in full_after_text.split()[0] if char.isalpha()]
+                after_text = "".join(after_text)
+                full_text = before_text + after_text
+                if check_word_spelling(full_text):
+                    text = full_before_text + full_after_text
+                else:
+                    text = full_before_text + " " + full_after_text
+                if match_token not in text:
+                    break
+            return text
+
+    text = "I- am- a go- od- boy"
+    eval_and_replace(text)
+  example_title: spell check
+- text: >
+    import torch
+
+    from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+    checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
+
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+
+    model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
+
+    sequences = ["I've been waiting for a HuggingFace course my whole life.",
+    "So have I!"]
+
+
+    tokens = tokenizer(sequences, padding=True, truncation=True,
+    return_tensors="pt")
 
+    output = model(**tokens)
+  example_title: model inference
 inference:
   parameters:
     max_length: 96