Update README.md

README.md

@@ -28,21 +28,19 @@ This model is suitable for English. -->
 
 
 
-- **Developed by:** "[orYx Models]"
-- **Funded by [optional]:** "[More Information Needed]"
-- **Shared by [optional]:** "[Vineedhar, relkino, kalhosni]"
-- **Model type:** {{ model_type | default("[Text Classifier]"
-- **Language(s) (NLP):** "[English]"
-- **License:** {{ license ("[MIT]"
-- **Finetuned from model [optional]:** "[cardiffnlp/twitter-roberta-base-sentiment-latest]"
+- **Developed by:** orYx Models
+- **Shared by [optional]:** Vineedhar, relkino, kalhosni
+- **Model type:** Text Classifier
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model [optional]:** cardiffnlp/twitter-roberta-base-sentiment-latest
 
 ### Model Sources [optional]
 
 <!--This is HuggingFace modelID - cardiffnlp/twitter-roberta-base-2021-124m-->
 
-- **Repository:** {{ repo | default("[More Information Needed]", true)}}
-- **Paper [optional]:** {{ paper | default("[TimeLMs - https://arxiv.org/abs/2202.03829]", true)}}
-- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
+- **Repository:** More Information Needed
+- **Paper [optional]:** TimeLMs - https://arxiv.org/abs/2202.03829
 
 ## Uses
 
@@ -54,22 +52,12 @@ Use case: We can analyse the text from any executive, employee, client of an org
 
 ### Direct Use
 
-<!-- You can infer the model at, orYx Models page, Leadership Sentiment Analyzer - spcace. 
-The Space id is - orYx-models/Leadership-sentiment-analyzer -->
+nlp = pipeline("sentiment-analysis", model = model, tokenizer = tokenizer)
 
-{{ direct_use | default("orYx-models/Leadership-sentiment-analyzer", true)}}
+nlp("The results don't match but the effort seems to be always high")
 
-### Downstream Use [optional]
+Out[7]: [{'label': 'Positive', 'score': 0.9996090531349182}]
 
-<!-- This phase is under progress and will be shared once the model is deployed under larger ecosystem/.app -->
-
-{{ downstream_use | default("[More Information Needed]", true)}}
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-{{ out_of_scope_use | default("[More Information Needed]", true)}}
 
 ## Bias, Risks, and Limitations
 
@@ -79,78 +67,118 @@ The Space id is - orYx-models/Leadership-sentiment-analyzer -->
 
 ### Recommendations
 
-<!-- ]You can futher finetune the model to get better results. -->
 
-{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}
 
-## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-{{ get_started_code | default("[More Information Needed]", true)}}
 
 ## Training Details
 
+
+
+
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+X_train, X_val, y_train, y_val = train_test_split(X,y, test_size = 0.2, stratify = y)
 
-{{ training_data | default("[More Information Needed]", true)}}
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
 
 #### Preprocessing [optional]
 
-{{ preprocessing | default("[More Information Needed]", true)}}
+'input_ids': tensor
+'attention_mask': tensor
+'label': tensor(2)
 
 
 #### Training Hyperparameters
 
-- **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+args = TrainingArguments(
+    output_dir="output",
+    do_train = True,
+    do_eval = True,
+    num_train_epochs = 1,
+    per_device_train_batch_size = 4,
+    per_device_eval_batch_size = 8,
+    warmup_steps = 50,
+    weight_decay = 0.01,
+    logging_strategy= "steps",
+    logging_dir= "logging",
+    logging_steps = 50,
+    eval_steps = 50,
+    save_strategy = "steps",
+    fp16 = True,
+    #load_best_model_at_end = True
+)
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+TrainOutput(global_step=879, 
+training_loss=0.1825900522650848, 
+metrics={'train_runtime': 101.6309, 
+'train_samples_per_second': 34.596, 
+'train_steps_per_second': 8.649, 
+'total_flos': 346915041274368.0, 
+'train_loss': 0.1825900522650848, 
+'epoch': 1.0})
 
-{{ speeds_sizes_times | default("[More Information Needed]", true)}}
+### Testing Data
 
-## Evaluation
+20%, 789 points off 4396 population of the Dataset.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
+#### Metrics
 
-#### Testing Data
+Accuracy
+F1 Score
+Precision
+Recall
 
-<!-- This should link to a Dataset Card if possible. -->
 
-{{ testing_data | default("[More Information Needed]", true)}}
+## Evaluation Results
 
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+loss  
+train   0.049349       
+val     0.108378 
 
-{{ testing_factors | default("[More Information Needed]", true)}}
+Accuracy 
+train  0.988908  
+val    0.976136  
 
-#### Metrics
+F1  
+train 0.987063       
+val   0.972464 
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-{{ testing_metrics | default("[More Information Needed]", true)}}
+Precision  
+train 0.982160    
+val   0.965982 
 
-### Results
+Recall
+train  0.992357
+val    0.979861
 
-{{ results | default("[More Information Needed]", true)}}
 
 #### Summary
 
-{{ results_summary | default("", true) }}
+Accuracy 
+train  98.8% 
+val    97.6%  
+
+F1  
+train  98.7%      
+val    97.2% 
 
-## Model Examination [optional]
+Precision  
+train  98.2%    
+val    96.5% 
+
+Recall
+train  99.2%
+val    97.9%
 
-<!-- Relevant interpretability work for the model goes here -->
 
 {{ model_examination | default("[More Information Needed]", true)}}
 
@@ -160,56 +188,45 @@ Use the code below to get started with the model.
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** {{ hardware_type | default("[More Information Needed]", true)}}
-- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
-- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
-- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
-- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
+- **Hardware Type:** T4 GPU
+- **Hours used:** 2 
+- **Cloud Provider:** Google
+- **Compute Region:** India
+- **Carbon Emitted:** No Information Available
 
-{{ model_specs | default("[More Information Needed]", true)}}
 
 ### Compute Infrastructure
 
-{{ compute_infrastructure | default("[More Information Needed]", true)}}
-
-#### Hardware
-
-{{ hardware_requirements | default("[More Information Needed]", true)}}
-
-#### Software
-
-{{ software | default("[More Information Needed]", true)}}
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-{{ citation_bibtex | default("[More Information Needed]", true)}}
-
-**APA:**
-
-{{ citation_apa | default("[More Information Needed]", true)}}
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-{{ glossary | default("[More Information Needed]", true)}}
-
-## More Information [optional]
+Google Colab - T4 GPU
+
+### References 
+```
+@inproceedings{camacho-collados-etal-2022-tweetnlp,
+    title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
+    author = "Camacho-collados, Jose  and
+      Rezaee, Kiamehr  and
+      Riahi, Talayeh  and
+      Ushio, Asahi  and
+      Loureiro, Daniel  and
+      Antypas, Dimosthenis  and
+      Boisson, Joanne  and
+      Espinosa Anke, Luis  and
+      Liu, Fangyu  and
+      Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, UAE",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.emnlp-demos.5",
+    pages = "38--49"
+}
 
-{{ more_information | default("[More Information Needed]", true)}}
 
 ## Model Card Authors [optional]
 
-{{ model_card_authors | default("[Vineedhar, relkino]", true)}}
+Vineedhar, relkino
 
 ## Model Card Contact
 
-{{ model_card_contact | default("[https://khalidalhosni.com/]", true)}}
+https://khalidalhosni.com/