Librarian Bot: Add base_model information to model

README.md

@@ -1,134 +1,74 @@
 ---
+language:
+- en
 license:
 - apache-2.0
 - cc-by-nc-4.0
-datasets: pszemraj/fleece2instructions-codealpaca
 tags:
 - generated_from_trainer
 - instruct
 - instructions
 - code
 - instructiongen
+datasets: pszemraj/fleece2instructions-codealpaca
 metrics:
 - rouge
-language:
-- en
 widget:
-- text: |
-    git lfs install
+- 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 ?? {};
-      }
-    }
+- text: "export interface DocumentParams {\n  pageContent: string;\n\n  // eslint-disable-next-line\
+    \ @typescript-eslint/no-explicit-any\n  metadata: Record<string, any>;\n}\n\n\
+    /**\n * Interface for interacting with a document.\n */\nexport class Document\
+    \ implements DocumentParams {\n  pageContent: string;\n\n  // eslint-disable-next-line\
+    \ @typescript-eslint/no-explicit-any\n  metadata: Record<string, any>;\n\n  constructor(fields?:\
+    \ Partial<DocumentParams>) {\n    this.pageContent = fields?.pageContent ?? this.pageContent;\n\
+    \    this.metadata = fields?.metadata ?? {};\n  }\n}\n"
   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
+- text: "def merge(left, right):\n    if len(left) == 0:\n        return right\n\n\
+    \    if len(right) == 0:\n        return left\n\n    result = []\n    index_left\
+    \ = index_right = 0\n\n    while len(result) < len(left) + len(right):\n     \
+    \   if left[index_left] <= right[index_right]:\n            result.append(left[index_left])\n\
+    \            index_left += 1\n        else:\n            result.append(right[index_right])\n\
+    \            index_right += 1\n\n        if index_right == len(right):\n     \
+    \       result += left[index_left:]\n            break\n\n        if index_left\
+    \ == len(left):\n            result += right[index_right:]\n            break\n\
+    \n    return result\n"
   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()
+- text: "import pandas as pd\nimport plotly.graph_objects as go\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')\n\
+    \nfig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],\n           \
+    \       name='Share Prices (in USD)'))\n\nfig.update_layout(title='Apple Share\
+    \ Prices over time (2014)',\n                   plot_bgcolor='rgb(230, 230,230)',\n\
+    \                   showlegend=True)\n\nfig.show()\n"
   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)
+- text: "from spellchecker import SpellChecker\n\nspell = SpellChecker()\n\ndef check_word_spelling(word:\
+    \ str):\n    misspelled = spell.unknown([word])\n    return len(misspelled) ==\
+    \ 0\n\ndef eval_and_replace(text: str, match_token: str = \"- \"):\n    if match_token\
+    \ not in text:\n        return text\n    else:\n        while True:\n        \
+    \    full_before_text = text.split(match_token, maxsplit=1)[0]\n            before_text\
+    \ = [\n                char for char in full_before_text.split()[-1] if char.isalpha()\n\
+    \            ]\n            before_text = \"\".join(before_text)\n           \
+    \ full_after_text = text.split(match_token, maxsplit=1)[-1]\n            after_text\
+    \ = [char for char in full_after_text.split()[0] if char.isalpha()]\n        \
+    \    after_text = \"\".join(after_text)\n            full_text = before_text +\
+    \ after_text\n            if check_word_spelling(full_text):\n               \
+    \ text = full_before_text + full_after_text\n            else:\n             \
+    \   text = full_before_text + \" \" + full_after_text\n            if match_token\
+    \ not in text:\n                break\n        return text\n\ntext = \"I- am-\
+    \ a go- od- boy\"\neval_and_replace(text)\n"
   example_title: spell check
-- text: >
-    import torch
+- text: 'import torch
 
     from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
@@ -139,19 +79,21 @@ widget:
 
     model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
 
-    sequences = ["I've been waiting for a HuggingFace course my whole life.",
-    "So have I!"]
+    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")
+    tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
 
     output = model(**tokens)
+
+    '
   example_title: model inference
 inference:
   parameters:
     max_length: 96
     num_beams: 4
+base_model: facebook/bart-large
 ---