Update README.md

README.md

@@ -10,7 +10,7 @@ license: mit
 
 # Model Card for "DiagTrast-Berto"
 
-This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) that is a BERT model trained on a big Spanish corpus.
+This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), which is a BERT model trained on a big Spanish corpus.
 
 DiagTrast-Berto was trained with [hackathon-somos-nlp-2023/DiagTrast](https://huggingface.co/datasets/hackathon-somos-nlp-2023/DiagTrast) dataset to classify statements with each of the 5 selected mental disorders of the DSM-5. While this task is classically approached with neural network-based models, the goal of implementing a transformer model is that instead of basing the classification criteria on keyword search, it is expected to understand natural language.
 
@@ -30,11 +30,7 @@ This model should not be used as a replacement for a mental health professional
 
 The main limitation of the model is that it is restricted to the identification of only 5 of the DSM-5 disorders.
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-[More Information Needed]
+Also, the model will always match a statement with a disorder since there was not a 'non-disorder' label in the dataset.
 
 ## How to Get Started with the Model
 
@@ -66,61 +62,16 @@ We use the [hackathon-somos-nlp-2023/DiagTrast](https://huggingface.co/datasets/
 
 ### Training Procedure 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-[More Information Needed]
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-#### Preprocessing [optional]
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-[More Information Needed]
+We use HuggingFace's Transformers library to load [BERTO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) checkpoint and fine-tune the model.
 
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
+We use the default ones.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Data Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The valuation dataset consists of 134 arbitrarily selected examples, so labels may not be in the same proportion. We use 'Accuracy' as our metric, achieving a 97% accuracy after 3 epochs. 
 
 ## Environmental Impact
 
@@ -133,37 +84,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Cloud Provider:** Google
 - **Compute Region:** Spain
 - **Carbon Emitted:** 0.005 kg C02
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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+  
 ## Team members
 
 - [Alberto Martín Garrido](https://huggingface.co/Stremie)