Librarian Bot: Add base_model information to model
Browse filesThis pull request aims to enrich the metadata of your model by adding [`facebook/bart-base`](https://huggingface.co/facebook/bart-base) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.
How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.
**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.
For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).
This PR was requested via the [Librarian Bot](https://huggingface.co/librarian-bot) [metadata request service](https://huggingface.co/spaces/librarian-bots/metadata_request_service) by request of [davanstrien](https://huggingface.co/davanstrien)
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---
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license:
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- apache-2.0
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- cc-by-nc-4.0
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datasets: pszemraj/fleece2instructions-codealpaca
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tags:
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- generated_from_trainer
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- instruct
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- instructions
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- code
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- instructiongen
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metrics:
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- rouge
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language:
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- en
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widget:
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- text:
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huggingface-cli lfs-enable-largefiles .
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git lfs track "*.bin"
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git add .
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git commit -a -m "add fp32 chkpt"
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git push
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example_title: bash
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- text:
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}
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/**
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* Interface for interacting with a document.
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*/
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export class Document implements DocumentParams {
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pageContent: string;
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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metadata: Record<string, any>;
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constructor(fields?: Partial<DocumentParams>) {
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this.pageContent = fields?.pageContent ?? this.pageContent;
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this.metadata = fields?.metadata ?? {};
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}
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}
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example_title: js
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- text:
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if len(right)
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index_left = index_right = 0
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while len(result) < len(left) + len(right):
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if left[index_left] <= right[index_right]:
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result.append(left[index_left])
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index_left += 1
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else:
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result.append(right[index_right])
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index_right += 1
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if index_right == len(right):
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result += left[index_left:]
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break
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if index_left == len(left):
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result += right[index_right:]
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break
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return result
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example_title: merge
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df =
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pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
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fig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],
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name='Share Prices (in USD)'))
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fig.update_layout(title='Apple Share Prices over time (2014)',
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plot_bgcolor='rgb(230, 230,230)',
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showlegend=True)
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fig.show()
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example_title: plot
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before_text = [
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char for char in full_before_text.split()[-1] if char.isalpha()
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]
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before_text = "".join(before_text)
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full_after_text = text.split(match_token, maxsplit=1)[-1]
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after_text = [char for char in full_after_text.split()[0] if char.isalpha()]
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after_text = "".join(after_text)
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full_text = before_text + after_text
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if check_word_spelling(full_text):
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text = full_before_text + full_after_text
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else:
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text = full_before_text + " " + full_after_text
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if match_token not in text:
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break
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return text
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text = "I- am- a go- od- boy"
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eval_and_replace(text)
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example_title: spell check
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- text:
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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@@ -139,19 +79,21 @@ widget:
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
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sequences = ["I've been waiting for a HuggingFace course my whole life.",
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tokens = tokenizer(sequences, padding=True, truncation=True,
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return_tensors="pt")
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output = model(**tokens)
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example_title: model inference
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inference:
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parameters:
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max_length: 96
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num_beams: 4
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---
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---
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language:
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- en
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license:
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- apache-2.0
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- cc-by-nc-4.0
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tags:
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- generated_from_trainer
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- instruct
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- instructions
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- code
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- instructiongen
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datasets: pszemraj/fleece2instructions-codealpaca
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metrics:
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- rouge
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widget:
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- text: 'git lfs install
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huggingface-cli lfs-enable-largefiles .
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git lfs track "*.bin"
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git add .
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git commit -a -m "add fp32 chkpt"
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git push
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'
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example_title: bash
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- text: "export interface DocumentParams {\n pageContent: string;\n\n // eslint-disable-next-line\
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\ @typescript-eslint/no-explicit-any\n metadata: Record<string, any>;\n}\n\n\
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/**\n * Interface for interacting with a document.\n */\nexport class Document\
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\ implements DocumentParams {\n pageContent: string;\n\n // eslint-disable-next-line\
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\ @typescript-eslint/no-explicit-any\n metadata: Record<string, any>;\n\n constructor(fields?:\
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\ Partial<DocumentParams>) {\n this.pageContent = fields?.pageContent ?? this.pageContent;\n\
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\ this.metadata = fields?.metadata ?? {};\n }\n}\n"
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example_title: js
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- text: "def merge(left, right):\n if len(left) == 0:\n return right\n\n\
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\ if len(right) == 0:\n return left\n\n result = []\n index_left\
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\ = index_right = 0\n\n while len(result) < len(left) + len(right):\n \
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\ if left[index_left] <= right[index_right]:\n result.append(left[index_left])\n\
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\ index_left += 1\n else:\n result.append(right[index_right])\n\
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\ index_right += 1\n\n if index_right == len(right):\n \
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\ result += left[index_left:]\n break\n\n if index_left\
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\ == len(left):\n result += right[index_right:]\n break\n\
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\n return result\n"
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example_title: merge
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- 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\
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\nfig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],\n \
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\ name='Share Prices (in USD)'))\n\nfig.update_layout(title='Apple Share\
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\ Prices over time (2014)',\n plot_bgcolor='rgb(230, 230,230)',\n\
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\ showlegend=True)\n\nfig.show()\n"
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example_title: plot
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- text: "from spellchecker import SpellChecker\n\nspell = SpellChecker()\n\ndef check_word_spelling(word:\
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\ str):\n misspelled = spell.unknown([word])\n return len(misspelled) ==\
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\ 0\n\ndef eval_and_replace(text: str, match_token: str = \"- \"):\n if match_token\
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\ not in text:\n return text\n else:\n while True:\n \
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\ full_before_text = text.split(match_token, maxsplit=1)[0]\n before_text\
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\ = [\n char for char in full_before_text.split()[-1] if char.isalpha()\n\
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\ ]\n before_text = \"\".join(before_text)\n \
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\ full_after_text = text.split(match_token, maxsplit=1)[-1]\n after_text\
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\ = [char for char in full_after_text.split()[0] if char.isalpha()]\n \
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\ after_text = \"\".join(after_text)\n full_text = before_text +\
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\ after_text\n if check_word_spelling(full_text):\n \
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\ text = full_before_text + full_after_text\n else:\n \
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\ text = full_before_text + \" \" + full_after_text\n if match_token\
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\ not in text:\n break\n return text\n\ntext = \"I- am-\
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\ a go- od- boy\"\neval_and_replace(text)\n"
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example_title: spell check
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- text: 'import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
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sequences = ["I''ve been waiting for a HuggingFace course my whole life.", "So
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have I!"]
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tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
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output = model(**tokens)
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'
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example_title: model inference
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inference:
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parameters:
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max_length: 96
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num_beams: 4
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base_model: facebook/bart-base
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---
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