Update README.md

README.md

@@ -6,19 +6,22 @@ tags:
 - text-classification
 - generated_from_setfit_trainer
 metrics:
-- metric
+- f1
+- accuracy
 widget:
-- text: A combined 20 million people per year die of smoking and hunger,  so authorities
-    can't seem to feed people and they allow you to buy cigarettes but we are facing
-    another lockdown for a virus that has a 99.5% survival rate!!! THINK PEOPLE. LOOK
-    AT IT LOGICALLY WITH YOUR OWN EYES.
-- text: Scientists do not agree on the consequences of climate change, nor is there
-    any consensus on that subject. The predictions on that from are just ascientific
-    speculation. Bring on the warming."
-- text: If Tam is our "top doctor"....I am going back to leaches and voodoo...just
+- text: >-
+    A combined 20 million people per year die of smoking and hunger,  so
+    authorities can't seem to feed people and they allow you to buy cigarettes
+    but we are facing another lockdown for a virus that has a 99.5% survival
+    rate!!! THINK PEOPLE. LOOK AT IT LOGICALLY WITH YOUR OWN EYES.
+- text: >-
+    Scientists do not agree on the consequences of climate change, nor is there
+    any consensus on that subject. The predictions on that from are just
+    ascientific speculation. Bring on the warming."
+- text: >-
+    If Tam is our "top doctor"....I am going back to leaches and voodoo...just
     as much science in that as the crap she spouts
-- text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions\
-    \ and just a good actor."
+- text: "Can she skip school by herself and sit infront of parliament? \r\n Fake emotions and just a good actor."
 - text: my dad had huge ones..so they may be real..
 pipeline_tag: text-classification
 inference: false
@@ -37,318 +40,69 @@ model-index:
     - type: metric
       value: 0.688144336139226
       name: Metric
+license: mit
+language:
+- en
 ---
 
-# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+# Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A ClassifierChain instance is used for classification.
+The official trained models for **"Computational Analysis of Communicative Acts for Understanding Crisis News Comment Discourses"**. 
 
-The model has been trained using an efficient few-shot learning technique that involves:
+This model is based on **SetFit** ([SetFit: Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)) and uses the **sentence-transformers/paraphrase-mpnet-base-v2** pretrained model. It has been fine-tuned on our **crisis narratives dataset**.
 
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
-
-## Model Details
-
-### Model Description
-- **Model Type:** SetFit
-- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
-- **Classification head:** a ClassifierChain instance
-- **Maximum Sequence Length:** 512 tokens
-<!-- - **Number of Classes:** Unknown -->
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
-- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
-- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
-
-## Evaluation
-
-### Metrics
-| Label   | Metric |
-|:--------|:-------|
-| **all** | 0.6881 |
-
-## Uses
-
-### Direct Use for Inference
-
-First install the SetFit library:
-
-```bash
-pip install setfit
-```
-
-Then you can load this model and run inference.
-
-```python
-from setfit import SetFitModel
-
-# Download from the 🤗 Hub
-model = SetFitModel.from_pretrained("CrisisNarratives/setfit-9classes-multi_label")
-# Run inference
-preds = model("my dad had huge ones..so they may be real..")
-```
-
-<!--
-### Downstream Use
-
-*List how someone could finetune this model on their own dataset.*
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
+---
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+### Model Information
+
+- **Architecture:** SetFit with sentence-transformers/paraphrase-mpnet-base-v2  
+- **Task:** Multi-label classification for communicative act actions  
+- **Classes:**  
+  - `informing statement`  
+  - `challenge`
+  - `accusation`
+  - `rejection`  
+  - `appreciation`  
+  - `request`  
+  - `question`  
+  - `acceptance`  
+  - `apology`  
 
-## Training Details
+---
 
-### Training Set Metrics
-| Training set | Min | Median  | Max  |
-|:-------------|:----|:--------|:-----|
-| Word count   | 1   | 25.8891 | 1681 |
+### How to Use the Model
 
-### Training Hyperparameters
-- batch_size: (16, 16)
-- num_epochs: (3, 3)
-- max_steps: -1
-- sampling_strategy: oversampling
-- num_iterations: 40
-- body_learning_rate: (1.752e-05, 1.752e-05)
-- head_learning_rate: 1.752e-05
-- loss: CosineSimilarityLoss
-- distance_metric: cosine_distance
-- margin: 0.25
-- end_to_end: False
-- use_amp: False
-- warmup_proportion: 0.1
-- seed: 30
-- eval_max_steps: -1
-- load_best_model_at_end: False
+You can find the code to fine-tune this model and detailed instructions in the following GitHub repository:  
+[Acts in Crisis Narratives - SetFit Fine-Tuning Notebook](https://github.com/Aalto-CRAI-CIS/Acts-in-crisis-narratives/blob/main/few_shot_learning/SetFit.ipynb)
 
-### Training Results
-| Epoch  | Step | Training Loss | Validation Loss |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.0004 | 1    | 0.3395        | -               |
-| 0.0185 | 50   | 0.3628        | -               |
-| 0.0370 | 100  | 0.2538        | -               |
-| 0.0555 | 150  | 0.2044        | -               |
-| 0.0739 | 200  | 0.1831        | -               |
-| 0.0924 | 250  | 0.2218        | -               |
-| 0.1109 | 300  | 0.2014        | -               |
-| 0.1294 | 350  | 0.2405        | -               |
-| 0.1479 | 400  | 0.1238        | -               |
-| 0.1664 | 450  | 0.1658        | -               |
-| 0.1848 | 500  | 0.1974        | -               |
-| 0.2033 | 550  | 0.1565        | -               |
-| 0.2218 | 600  | 0.1131        | -               |
-| 0.2403 | 650  | 0.0994        | -               |
-| 0.2588 | 700  | 0.0743        | -               |
-| 0.2773 | 750  | 0.0259        | -               |
-| 0.2957 | 800  | 0.1852        | -               |
-| 0.3142 | 850  | 0.1896        | -               |
-| 0.3327 | 900  | 0.1102        | -               |
-| 0.3512 | 950  | 0.0951        | -               |
-| 0.3697 | 1000 | 0.0619        | -               |
-| 0.3882 | 1050 | 0.0227        | -               |
-| 0.4067 | 1100 | 0.0986        | -               |
-| 0.4251 | 1150 | 0.0375        | -               |
-| 0.4436 | 1200 | 0.1151        | -               |
-| 0.4621 | 1250 | 0.1128        | -               |
-| 0.4806 | 1300 | 0.0334        | -               |
-| 0.4991 | 1350 | 0.1012        | -               |
-| 0.5176 | 1400 | 0.0895        | -               |
-| 0.5360 | 1450 | 0.072         | -               |
-| 0.5545 | 1500 | 0.0619        | -               |
-| 0.5730 | 1550 | 0.0852        | -               |
-| 0.5915 | 1600 | 0.0611        | -               |
-| 0.6100 | 1650 | 0.0679        | -               |
-| 0.6285 | 1700 | 0.0238        | -               |
-| 0.6470 | 1750 | 0.1776        | -               |
-| 0.6654 | 1800 | 0.081         | -               |
-| 0.6839 | 1850 | 0.1059        | -               |
-| 0.7024 | 1900 | 0.045         | -               |
-| 0.7209 | 1950 | 0.0664        | -               |
-| 0.7394 | 2000 | 0.0666        | -               |
-| 0.7579 | 2050 | 0.0714        | -               |
-| 0.7763 | 2100 | 0.0312        | -               |
-| 0.7948 | 2150 | 0.0461        | -               |
-| 0.8133 | 2200 | 0.0946        | -               |
-| 0.8318 | 2250 | 0.047         | -               |
-| 0.8503 | 2300 | 0.0906        | -               |
-| 0.8688 | 2350 | 0.0186        | -               |
-| 0.8872 | 2400 | 0.0937        | -               |
-| 0.9057 | 2450 | 0.1674        | -               |
-| 0.9242 | 2500 | 0.0311        | -               |
-| 0.9427 | 2550 | 0.0884        | -               |
-| 0.9612 | 2600 | 0.0787        | -               |
-| 0.9797 | 2650 | 0.192         | -               |
-| 0.9982 | 2700 | 0.0689        | -               |
-| 1.0166 | 2750 | 0.0945        | -               |
-| 1.0351 | 2800 | 0.066         | -               |
-| 1.0536 | 2850 | 0.0592        | -               |
-| 1.0721 | 2900 | 0.068         | -               |
-| 1.0906 | 2950 | 0.0619        | -               |
-| 1.1091 | 3000 | 0.0329        | -               |
-| 1.1275 | 3050 | 0.0986        | -               |
-| 1.1460 | 3100 | 0.0468        | -               |
-| 1.1645 | 3150 | 0.0717        | -               |
-| 1.1830 | 3200 | 0.0721        | -               |
-| 1.2015 | 3250 | 0.0345        | -               |
-| 1.2200 | 3300 | 0.0317        | -               |
-| 1.2384 | 3350 | 0.0476        | -               |
-| 1.2569 | 3400 | 0.122         | -               |
-| 1.2754 | 3450 | 0.0576        | -               |
-| 1.2939 | 3500 | 0.0375        | -               |
-| 1.3124 | 3550 | 0.1074        | -               |
-| 1.3309 | 3600 | 0.113         | -               |
-| 1.3494 | 3650 | 0.0564        | -               |
-| 1.3678 | 3700 | 0.0437        | -               |
-| 1.3863 | 3750 | 0.0623        | -               |
-| 1.4048 | 3800 | 0.0213        | -               |
-| 1.4233 | 3850 | 0.0629        | -               |
-| 1.4418 | 3900 | 0.059         | -               |
-| 1.4603 | 3950 | 0.0807        | -               |
-| 1.4787 | 4000 | 0.0946        | -               |
-| 1.4972 | 4050 | 0.0381        | -               |
-| 1.5157 | 4100 | 0.0451        | -               |
-| 1.5342 | 4150 | 0.0742        | -               |
-| 1.5527 | 4200 | 0.0899        | -               |
-| 1.5712 | 4250 | 0.0722        | -               |
-| 1.5896 | 4300 | 0.1022        | -               |
-| 1.6081 | 4350 | 0.0446        | -               |
-| 1.6266 | 4400 | 0.022         | -               |
-| 1.6451 | 4450 | 0.0586        | -               |
-| 1.6636 | 4500 | 0.0585        | -               |
-| 1.6821 | 4550 | 0.0409        | -               |
-| 1.7006 | 4600 | 0.0253        | -               |
-| 1.7190 | 4650 | 0.0363        | -               |
-| 1.7375 | 4700 | 0.0492        | -               |
-| 1.7560 | 4750 | 0.0154        | -               |
-| 1.7745 | 4800 | 0.0427        | -               |
-| 1.7930 | 4850 | 0.0284        | -               |
-| 1.8115 | 4900 | 0.022         | -               |
-| 1.8299 | 4950 | 0.0335        | -               |
-| 1.8484 | 5000 | 0.0222        | -               |
-| 1.8669 | 5050 | 0.0291        | -               |
-| 1.8854 | 5100 | 0.0824        | -               |
-| 1.9039 | 5150 | 0.0563        | -               |
-| 1.9224 | 5200 | 0.0355        | -               |
-| 1.9409 | 5250 | 0.064         | -               |
-| 1.9593 | 5300 | 0.0596        | -               |
-| 1.9778 | 5350 | 0.0789        | -               |
-| 1.9963 | 5400 | 0.0901        | -               |
-| 2.0148 | 5450 | 0.0388        | -               |
-| 2.0333 | 5500 | 0.0738        | -               |
-| 2.0518 | 5550 | 0.0712        | -               |
-| 2.0702 | 5600 | 0.0825        | -               |
-| 2.0887 | 5650 | 0.0406        | -               |
-| 2.1072 | 5700 | 0.0623        | -               |
-| 2.1257 | 5750 | 0.0423        | -               |
-| 2.1442 | 5800 | 0.0566        | -               |
-| 2.1627 | 5850 | 0.0745        | -               |
-| 2.1811 | 5900 | 0.0271        | -               |
-| 2.1996 | 5950 | 0.0257        | -               |
-| 2.2181 | 6000 | 0.0347        | -               |
-| 2.2366 | 6050 | 0.0291        | -               |
-| 2.2551 | 6100 | 0.0401        | -               |
-| 2.2736 | 6150 | 0.0222        | -               |
-| 2.2921 | 6200 | 0.0217        | -               |
-| 2.3105 | 6250 | 0.0589        | -               |
-| 2.3290 | 6300 | 0.0685        | -               |
-| 2.3475 | 6350 | 0.1191        | -               |
-| 2.3660 | 6400 | 0.0626        | -               |
-| 2.3845 | 6450 | 0.0615        | -               |
-| 2.4030 | 6500 | 0.0327        | -               |
-| 2.4214 | 6550 | 0.0431        | -               |
-| 2.4399 | 6600 | 0.1037        | -               |
-| 2.4584 | 6650 | 0.0318        | -               |
-| 2.4769 | 6700 | 0.062         | -               |
-| 2.4954 | 6750 | 0.0183        | -               |
-| 2.5139 | 6800 | 0.0568        | -               |
-| 2.5323 | 6850 | 0.0581        | -               |
-| 2.5508 | 6900 | 0.0363        | -               |
-| 2.5693 | 6950 | 0.0413        | -               |
-| 2.5878 | 7000 | 0.076         | -               |
-| 2.6063 | 7050 | 0.046         | -               |
-| 2.6248 | 7100 | 0.0401        | -               |
-| 2.6433 | 7150 | 0.0552        | -               |
-| 2.6617 | 7200 | 0.0767        | -               |
-| 2.6802 | 7250 | 0.0167        | -               |
-| 2.6987 | 7300 | 0.0459        | -               |
-| 2.7172 | 7350 | 0.0306        | -               |
-| 2.7357 | 7400 | 0.0559        | -               |
-| 2.7542 | 7450 | 0.0688        | -               |
-| 2.7726 | 7500 | 0.0417        | -               |
-| 2.7911 | 7550 | 0.033         | -               |
-| 2.8096 | 7600 | 0.0404        | -               |
-| 2.8281 | 7650 | 0.0391        | -               |
-| 2.8466 | 7700 | 0.0254        | -               |
-| 2.8651 | 7750 | 0.0635        | -               |
-| 2.8835 | 7800 | 0.0739        | -               |
-| 2.9020 | 7850 | 0.0274        | -               |
-| 2.9205 | 7900 | 0.0394        | -               |
-| 2.9390 | 7950 | 0.0606        | -               |
-| 2.9575 | 8000 | 0.0098        | -               |
-| 2.9760 | 8050 | 0.0997        | -               |
-| 2.9945 | 8100 | 0.0369        | -               |
+#### Steps to Load and Use the Model:
 
-### Framework Versions
-- Python: 3.9.16
-- SetFit: 1.0.1
-- Sentence Transformers: 2.2.2
-- Transformers: 4.35.0
-- PyTorch: 2.1.0+cu121
-- Datasets: 2.14.6
-- Tokenizers: 0.14.1
+1. Install the SetFit library:  
+   ```bash
+   pip install setfit
+   ```
+   
+2. Load the model and run inference:  
+   ```python
+   from setfit import SetFitModel
 
-## Citation
+   # Download from the 🤗 Hub
+   model = SetFitModel.from_pretrained("CrisisNarratives/setfit-9classes-multi_label")
+   
+   # Run inference
+   preds = model("I'm sorry.")
+   ```
 
-### BibTeX
-```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-    doi = {10.48550/ARXIV.2209.11055},
-    url = {https://arxiv.org/abs/2209.11055},
-    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-    title = {Efficient Few-Shot Learning Without Prompts},
-    publisher = {arXiv},
-    year = {2022},
-    copyright = {Creative Commons Attribution 4.0 International}
-}
-```
+For detailed instructions, refer to the GitHub repository linked above.
 
-<!--
-## Glossary
+---
 
-*Clearly define terms in order to be accessible across audiences.*
--->
+### Citation
 
-<!--
-## Model Card Authors
+If you use this model in your work, please cite:  
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+##### TO BE ADDED.
 
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-## Model Card Contact
+### Questions or Feedback?
 
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