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docs: change outdated code links in README.md
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@arielge
arielge committed Jan 30, 2023
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Expand Up @@ -107,12 +107,12 @@ Alternatively, it is possible to override specific configuration parameters at s
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| `first_model_positive_threshold` | Number of elements that must be assigned a positive label for the category in order to trigger the training of a classification model. <br /> <br /> _See also:_ The [training invocation](https://www.label-sleuth.org/docs/dev/model_training.html#training-invocation) documentation. |
| `changed_element_threshold` | Number of changes in user labels for the category -- relative to the last trained model -- that are required to trigger the training of a new model. A change can be a assigning a label (positive or negative) to an element, or changing an existing label. Note that `first_model_positive_threshold` must also be met for the training to be triggered. <br /> <br /> _See also:_ The [training invocation](https://www.label-sleuth.org/docs/dev/model_training.html#training-invocation) documentation. |
| `training_set_selection_strategy` | Strategy to be used from [TrainingSetSelectionStrategy](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/training_set_selector/train_set_selector_api.py#L9). A TrainingSetSelectionStrategy determines which examples will be sent in practice to the classification models at training time - these will not necessarily be identical to the set of elements labeled by the user. For currently supported implementations see [get_training_set_selector()](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/training_set_selector/training_set_selector_factory.py#L7). <br /> <br /> _See also:_ The [training set selection](https://www.label-sleuth.org/docs/dev/model_training.html#training-set-selection) documentation. |
| `model_policy` | Policy to be used from [ModelPolicies](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/models/core/model_policies.py#L5). A [ModelPolicy](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/models/policy/model_policy.py#L6) determines which type of classification model(s) will be used, and _when_ (e.g. always / only after a specific number of iterations / etc.). <br /> <br /> _See also:_ The [model selection](https://www.label-sleuth.org/docs/dev/model_training.html#model-selection) documentation. |
| `active_learning_strategy` | Strategy to be used from [ActiveLearningStrategies](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/active_learning/core/active_learning_strategies.py#L4). An [ActiveLearner](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/active_learning/core/active_learning_api.py#L11) module implements the strategy for recommending the next elements to be labeled by the user, aiming to increase the efficiency of the annotation process. For currently supported implementations see [get_active_learner()](https://github.com/label-sleuth/label-sleuth/blob/316bacb7cca7d7b78a11b96d397aac9bfd7e33bf/label_sleuth/active_learning/core/active_learning_factory.py#L8). <br /> <br /> _See also:_ The [active learning](https://www.label-sleuth.org/docs/dev/active_learning.html) documentation. |
| `training_set_selection_strategy` | Strategy to be used from [TrainingSetSelectionStrategy](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/training_set_selector/train_set_selector_api.py#L24). A TrainingSetSelectionStrategy determines which examples will be sent in practice to the classification models at training time - these will not necessarily be identical to the set of elements labeled by the user. For currently supported implementations see [get_training_set_selector()](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/training_set_selector/training_set_selector_factory.py). <br /> <br /> _See also:_ The [training set selection](https://www.label-sleuth.org/docs/dev/model_training.html#training-set-selection) documentation. |
| `model_policy` | Policy to be used from [ModelPolicies](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/models/core/model_policies.py). A [ModelPolicy](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/models/policy/model_policy.py#L21) determines which type of classification model(s) will be used, and _when_ (e.g. always / only after a specific number of iterations / etc.). <br /> <br /> _See also:_ The [model selection](https://www.label-sleuth.org/docs/dev/model_training.html#model-selection) documentation. |
| `active_learning_strategy` | Strategy to be used from [ActiveLearningCatalog](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/active_learning/core/catalog.py#L22). An [ActiveLearner](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/active_learning/core/active_learning_api.py#L26) module implements the strategy for recommending the next elements to be labeled by the user, aiming to increase the efficiency of the annotation process. For currently supported implementations see the [ActiveLearningCatalog](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/active_learning/core/catalog.py#L22). <br /> <br /> _See also:_ The [active learning](https://www.label-sleuth.org/docs/dev/active_learning.html) documentation. |
| `precision_evaluation_size` | Sample size to be used for estimating the precision of the current model. To be used in future versions of the system, which will provide built-in evaluation capabilities. |
| `apply_labels_to_duplicate_texts` | Specifies how to treat elements with identical texts. If `true`, assigning a label to an element will also assign the same label to other elements which share the exact same text; if `false`, the label will only be assigned to the specific element labeled by the user. |
| `language` | Specifies the chosen system-wide language. This determines some language-specific resources that will be used by models and helper functions (e.g., stop words). The list of supported languages can be found in [Languages](https://github.com/label-sleuth/label-sleuth/blob/b7d60ac5e62448514d10f9de093c5c987bca2e96/label_sleuth/models/core/languages.py#L58). We welcome contributions of additional languages. |
| `language` | Specifies the chosen system-wide language. This determines some language-specific resources that will be used by models and helper functions (e.g., stop words). The list of supported languages can be found in [Languages](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/models/core/languages.py). We welcome contributions of additional languages. |
| `login_required` | Specifies whether or not using the system will require user authentication. If `true`, the configuration file must also include a `users` parameter |
| `users` | Only relevant if `login_required` is `true`. Specifies the pre-defined login information in the following format: <pre>"users":[<br>&nbsp;{<br>&nbsp;&nbsp;&nbsp;"username": "<predefined_username1>",<br>&nbsp;&nbsp;&nbsp;"token":"<randomly_generated_token1>",<br>&nbsp;&nbsp;&nbsp;"password":"<predefined_user1_password>"<br>&nbsp;}<br>] </pre> * The list of usernames is static and currently all users have access to all the workspaces in the system. |

Expand Down Expand Up @@ -154,7 +154,7 @@ Below are instructions for implementing new models and active learning strategie
- items_to_infer: a list of dictionaries with at least the "text" field. Additional fields can be passed,
e.g. *[{'text': 'text1', 'additional_field': 'value1'}, {'text': 'text2', 'additional_field': 'value2'}]*

Returns a list of [Prediction](https://github.com/label-sleuth/label-sleuth/blob/1424a9ab01697e12396bc33fd608158d61d55e24/label_sleuth/models/core/prediction.py#L20) objects - one for each item in *items_to_infer* - where
Returns a list of [Prediction](https://github.com/label-sleuth/label-sleuth/blob/main/label_sleuth/models/core/prediction.py#L20) objects - one for each item in *items_to_infer* - where
Prediction.label is a boolean and Prediction.score is a float in the range [0-1].
Additional outputs can be passed by inheriting from the base Prediction class and overriding the get_predictions_class() method.

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