made changes accroding to the review comments for inference only text models guide
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"source": [
- "For **multi-label classification**, we simply need to pass `multi_class=True` in the `predict()` method of the model. The resulting per label scores for multi-label classification are independent **probabilities** and fall in the **(0,1)** range."
+ "For **multi-label classification**, we simply need to pass `multi_class=True` in the `predict()` method of the model. The resulting per label scores for multi-label classification are independent probabilities and fall in the (0, 1) range."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "**FillMask** model can be used to provide suggestion for a missing token/word in a sentence. These models have been trained with a [Masked Language Modeling (MLM)](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) objective, which includes the bi-directional models in the library. Visit this [link](https://huggingface.co/models?filter=lm-head) to learn more about the available models for **fill-mask** task. To get a list of supported transformer backbones for this model use the below command."
+ "**FillMask** model can be used to provide suggestion for a missing token/word in a sentence. These models have been trained with a [Masked Language Modeling (MLM)](https://huggingface.co/transformers/task_summary.html#masked-language-modeling) objective, which includes the bi-directional models in the library. Visit this [link](https://huggingface.co/models?filter=lm-head) to learn more about the available models for **fill-mask** task. To get a list of supported transformer backbones for this model use the below command."
]
},
{
