This project provides access to over 15,000 neural network models provided by Hugging Face. An API key from huggingface.co (not .com) is required. A free plan provides 30,000 input characters per month. Examples include: * Translate between languages. * Classify text into categories you choose. * Produce a summary of text. * Hold a conversation. * Query a spreadsheet. * Answer a question given a context. * Detect how positive or negative some is. * Find the named entities in some text. * Fill in the blanks. data:image/png;base64,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This project provides access to over 15,000 neural network models provided by Hugging Face. An API key from huggingface.co (not .com) is required. A free plan provides 30,000 input characters per month. Examples include: * Translate between languages. * Classify text into categories you choose. * Produce a summary of text. * Hold a conversation. * Query a spreadsheet. * Answer a question given a context. * Detect how positive or negative some is. * Find the named entities in some text. * Fill in the blanks.

pt:um par (chave: _ , valor: _ )

pt:a resposta a _ de _ enviando _ e cabeçalhos _ GETGET POST PUT DELETEhttps://snap.berkeley.edu

Can communicate with over 15,000 models. The API key can be obtained from huggingface.co More information at https://ecraft2learn.github.io/ai/AI-Teacher-Guide/chapter-5.html#hugging-face

Check if there is an error

Reports the character position (starting from 1) of the beginning of the first input string inside the second input string. If it's not found, reports 0.

pt:a posição de _ em _ ca:posició de _ en _

Reports True if the first input string contains the second input string, otherwise false. Comparison is case-independent by default; use USE CASE-INDEPENDENT COMPARISONS to change that.

pt:o texto de _ antes de _ ca:text _ conté _ ?

Reports the input text with lower case letters instead of capital letters in the input. Uses the user's locale's rules for case conversion.

pt:em minúsculas _ ca:_ a minúscules

ca:comparacions ignorant majúscules? err_reset

Input can either be text or a list of texts.

truetruedb_get(key)Hugging Face

pt:o texto multilinha _ ca:multilínia _

if you entered your Hugging Face API key into the browser's local storage this will remove it.

Input are texts that are potential classes for inputs. (max 10 candidate_labels, for more, simply run multiple requests, results are going to be misleading if using too many candidate labels anyway. If you want to keep the exact same, you can simply run multi label=True and do the scaling on your end. )

"candidate_labels": [

]

If true then classes can overlap.

true

The minimum length in tokens of the output summary.

The maximum length in tokens of the output summary.

The top tokens considered within the sample operation to create new text.

Define the tokens that are within the sample` operation of text generation. Add tokens in the sample for more probable to least probable until the sum of the probabilities is greater than top p.

The temperature of the sampling operation. Value is between 0 and 100. 1 means regular sampling, 0 means top k=1, 100.0 is getting closer to uniform probability.

The more a token is used within generation the more it is penalized to not be picked in successive generation passes. Value is between 0 and 100.

The maximu amount of time in seconds that the query should take. Network can cause some overhead so it will be a soft limit. Value is between 0 and 120.

Input can either be text or a list of texts.

}

This can be used to answer several questions at once. If the first input is a list then the second input should have the same number of text elements.

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==Fills in the blank (indicated by [MASK] in text. More details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#text2text-generation-taskFinds named enties in some text. More details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#named-entity-recognition-ner-taskReports how positive or negative the text is. More details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#text-classification-taskThe following model takes a query and table and generates an answer. More details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#table-question-answering-taskinputHi. What do you want to talk about?A simple use of DialoGPT-large for a conversation. Note that this should really send only the last few inputs and responses to avoid using up too much of the character input quota.A version of GPT-2 trained for dialogs. 762M parameters 230 times smaller than GPT-3. More details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#conversational-taskInstead of a spreadsheet you can construct a table as a list of lists and use the following:Given some text and a list of labels this will score how well each label fits the text. If multi-label is true then it predicts the probability that each label is correct. Details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#zero-shot-classification-taskReports the part of speech for each word in a sentence. The tags are described here https://huggingface.co/flair/pos-englishProduces a summary of some text. Length, top k, top p, temperature, repetition penality, and maximum time can optionally be controlled as parameters. Details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#summarization-task The example below is copied from Wikipedia. Note the misunderstanding of "codification".Given a context, answers a question. More details at https://api-inference.huggingface.co/docs/curl/html/detailed_parameters.html#question-answering-taskTranslates text from English to French (en-fr) Over 1300 translation models can be found at https://huggingface.co/Helsinki-NLPBelow are examples of using a few of Hugging Face's over 20,000 models. You will need an API token from huggingface.co to run these.