Update README.md

README.md

@@ -1,3 +1,74 @@
 ---
-license: mit
+language:
+- en
+license: mit  # Example: apache-2.0 or any license from https://huggingface.co/docs/hub/model-repos#list-of-license-identifiers
+tags:
+- text  # Example: audio
+- Twitter
+datasets:
+- CLPsych 2015  # Example: common_voice. Use dataset id from https://hf.co/datasets
+metrics:
+- accuracy, f1, precision, recall, AUC # Example: wer. Use metric id from https://hf.co/metrics
+
+model-index:
+- name: distilbert-depression-mixed
+  results: []
 ---
+
+# distilbert-depression-mixed
+
+This model is a fine-tuned version of [base-uncased](https://huggingface.co/distilbert-base-uncased) trained on CLPsych 2015 and a scraped dataset, and evaluated on a scraped dataset from Twitter.
+It achieves the following results on the evaluation set:
+- Evaluation Loss: 0.71
+- Accuracy: 0.63
+- F1: 0.59
+- Precision: 0.66
+- Recall: 0.53
+- AUC: 0.63
+
+
+## Intended uses & limitations
+
+Feed a corpus of tweets to the model to generate label if input is indicative of depression or not. Label 1 is depression, Label 0 is not.
+
+Limitation: All token sequences longer than 512 are automatically truncated. Also, training and test data may be contaminated with mislabeled users.
+
+### How to use
+
+You can use this model directly with a pipeline for sentiment analysis:
+
+```python
+>>> from transformers import DistilBertTokenizerFast, AutoTokenizer
+>>> tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+>>> from transformers import DistilBertForSequenceClassification
+>>> model = DistilBertForSequenceClassification.from_pretrained(r"distilbert-depression-mixed")
+>>> from transformers import pipeline
+>>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+>>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
+>>> result=classifier('pain peko',**tokenizer_kwargs) #For truncation to apply in the pipeline
+
+
+[{'label': 'LABEL_1', 'score': 0.5048992037773132}]
+```
+
+Otherwise, download the files and specify within the pipeline the path to the folder that contains the config.json, pytorch_model.bin, and training_args.bin
+
+## Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 4.19e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- weight_decay: 0.06
+- num_epochs: 5.0
+
+## Training results
+
+
+| Epoch | Training Loss | Validation Loss | Accuracy |    F1    | Precision |  Recall  |    AUC   |
+|:-----:|:-------------:|:---------------:|:--------:|:--------:|:---------:|:--------:|:--------:|
+| 1.0   | 0.68          | 0.66            | 0.61     | 0.54     | 0.60      | 0.50     | 0.60     |
+| 2.0   | 0.65          | 0.65            | 0.63     | 0.49     | 0.70      | 0.37     | 0.62     |
+| 3.0   | 0.53          | 0.63            | 0.66     | 0.58     | 0.69      | 0.50     | 0.65     |
+| 4.0   | 0.39          | 0.66            | 0.67     | 0.61     | 0.69      | 0.54     | 0.67     |
+| 5.0   | 0.27          | 0.72            | 0.65     | 0.61     | 0.63      | 0.60     | 0.64     |