Update README.md

readme adapted to specific model

README.md

@@ -12,8 +12,6 @@ license: mit
 # deberta-v3-large-zeroshot-v1.1-all-33
 ## Model description
 The model is designed for zero-shot classification with the Hugging Face pipeline. 
-The model should be substantially better at zero-shot classification than my other zero-shot models on the
-Hugging Face hub: https://huggingface.co/MoritzLaurer.
 
 The model can do one universal task: determine whether a hypothesis is `true` or `not_true`
 given a text (also called `entailment` vs. `not_entailment`).  
@@ -21,17 +19,18 @@ This task format is based on the Natural Language Inference task (NLI).
 The task is so universal that any classification task can be reformulated into the task.
 
 ## Training data
-The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format.
-1.   26 classification tasks with ~400k texts:
+The model was trained on a mixture of 33 datasets and 389 classes that have been reformatted into this universal format.
+1.   Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
+2.   28 classification tasks with ~51k texts:
 'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
 'emotiondair', 'emocontext', 'empathetic',
 'financialphrasebank', 'banking77', 'massive',
 'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate', 
 'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
 'agnews', 'yahootopics',
-'trueteacher', 'spam', 'wellformedquery'.
-See details on each dataset here: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
-3.   Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
+'trueteacher', 'spam', 'wellformedquery',
+'manifesto', 'capsotu'.
+See details on each dataset here: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv
 
 Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
 as opposed to three classes (entailment/neutral/contradiction)
@@ -41,10 +40,11 @@ as opposed to three classes (entailment/neutral/contradiction)
 #### Simple zero-shot classification pipeline
 ```python
 from transformers import pipeline
-classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1")
-sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
-candidate_labels = ["politics", "economy", "entertainment", "environment"]
-output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
+text = "Angela Merkel is a politician in Germany and leader of the CDU"
+hypothesis_template = "This example is about {}"
+classes_verbalized = ["politics", "economy", "entertainment", "environment"]
+zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33")
+output = zeroshot_classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False)
 print(output)
 ```
 
@@ -60,12 +60,10 @@ Please consult the original DeBERTa paper and the papers for the different datas
 The base model (DeBERTa-v3) is published under the MIT license.
 The datasets the model was fine-tuned on are published under a diverse set of licenses.
 The following spreadsheet provides an overview of the non-NLI datasets used for fine-tuning.
-The spreadsheets contains information on licenses, the underlying papers etc.: https://docs.google.com/spreadsheets/d/1Z18tMh02IiWgh6o8pfoMiI_LH4IXpr78wd_nmNd5FaE/edit?usp=sharing
-
-In addition, the model was also trained on the following NLI datasets: MNLI, ANLI, WANLI, LING-NLI, FEVER-NLI.
+The spreadsheets contains information on licenses, the underlying papers etc.: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv
 
 ## Citation
-If you use this model, please cite: 
+If you use this model academically, please cite: 
 ```
 @article{laurer_less_2023,
 	title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
@@ -87,25 +85,25 @@ If you use this model, please cite:
 If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
 
 ### Debugging and issues
-Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.
+Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers can have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.
 
 ### Hypotheses used for classification
-
-These hypotheses were used to fine-tune the model. 
-Inspecting them can help users get a feeling for which type of hypotheses and tasks the model was trained on. 
-I recommend formulating your hypotheses in a similar format. For example:
+The hypotheses in the tables below were used to fine-tune the model. 
+Inspecting them can help users get a feeling for which type of hypotheses and tasks the model was trained on.
+You can formulate your own hypotheses by changing the `hypothesis_template` of the zeroshot pipeline. For example:
 
 ```python
 from transformers import pipeline
 text = "Angela Merkel is a politician in Germany and leader of the CDU"
-classes_verbalized = ["politics", "economy", "entertainment", "environment"]
-hypothesis_template = "This example is about {}"
-model_name = "MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33"
-classifier = pipeline("zero-shot-classification", model=model_name)
-output = classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False)
+hypothesis_template = "Merkel is the leader of the party: {}"
+classes_verbalized = ["CDU", "SPD", "Greens"]
+zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33")
+output = zeroshot_classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False)
 print(output)
 ```
 
+Note that a few rows in the `massive` and `banking77` datasets contain `nan` because some classes were so ambiguous/unclear that I excluded them from the data. 
+
 
 #### wellformedquery
 | label           | hypothesis                                     |