Update README.md

README.md

@@ -1,202 +1,191 @@
 ---
 base_model: AI-Sweden-Models/gpt-sw3-1.3b
 library_name: peft
+datasets:
+- barbaroo/Sprotin_parallel
+language:
+- en
+- fo
+metrics:
+- bleu
+- chrf
+- bertscore
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+ Model Card: English–Faroese Translation Adapter
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
+**Model Description**
 
+- **Developed by:** Barbara Scalvini
+- **Model type:** Language model adapter for **English → Faroese** translation  
+- **Language(s):** English, Faroese  
+- **License:** This adapter inherits the license from the original GPT-SW3 1.3 B model.
+- **Finetuned from model:** [AI-Sweden-Models/gpt-sw3-1.3b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b)  
+- **Library used:** [PEFT 0.13.0](https://github.com/huggingface/peft)
 
+### Model Sources 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Paper:** [COMING SOON]  
+---
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This adapter is intended to perform **English→Faroese** translation, leveraging a **parameter-efficient fine-tuning** (PEFT) approach.
 
 ### Downstream Use [optional]
+- Can be integrated into broader **multilingual** or **localization** workflows.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
 
 ### Out-of-Scope Use
+- Any uses that rely on languages other than **English or Faroese** will likely yield suboptimal results.
+- Other tasks (e.g., summarization, classification) may be unsupported or require further fine-tuning.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
+- **Biases:** The model could reflect **biases** present in the training data, such as historical or societal biases in English or Faroese texts.
+- **Recommendation:** Users should **critically evaluate** outputs, especially in sensitive or high-stakes applications.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+---
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+import torch
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+import pandas as pd
+
+ADAPTER_REPO = "barbaroo/gptsw3_translate_1.3B"
+BASE_MODEL = "AI-Sweden-Models/gpt-sw3-1.3b"
+
+# 1. Load the tokenizer from the base model
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+
+model = AutoPeftModelForCausalLM.from_pretrained(
+    ADAPTER_REPO,
+    load_in_8bit=True,             # Optional: 8-bit quantization for GPU memory efficiency
+    device_map="auto",             # Automatically spread layers across available GPUs
+)
+
+# Ensure the model is in evaluation mode
+model.eval()
+
+# Alpaca-style prompt template
+alpaca_prompt = """
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}
+"""
+
+# EOS token from the tokenizer
+EOS_TOKEN = tokenizer.eos_token 
+print(EOS_TOKEN)
+
+sentences =  ['hello world']
+
+translations = []
+
+for sentence in sentences:
+    # Tokenize the input sentence and prepare the prompt for each sentence
+    inputs = tokenizer(
+        [
+            alpaca_prompt.format(
+                "Translate this sentence from English to Faroese:",  # instruction
+                sentence,  # input sentence to translate
+                "",  # output - leave blank for generation
+            )
+        ], 
+        return_tensors="pt"
+    ).to("cuda")
+
+    # Generate the output
+    outputs = model.generate(**inputs,
+                             max_new_tokens=2000, 
+                             eos_token_id=tokenizer.eos_token_id,  # Ensure EOS token is used
+                             pad_token_id=tokenizer.pad_token_id,  # Ensure padding token is used
+                             use_cache=True,
+                             do_sample = True,
+                             temperature = 0.1,
+                             top_p=1)
+
+    # Decode the generated tokens into a string
+    output_string = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
+    #print(output_string)
+
+    # Use a regular expression to extract the response part
+    try:
+        spl_word_1 = 'Response:\n'
+        res =  output_string.split(spl_word_1, 1)
+        response = res[1]
+        translation = response.replace(EOS_TOKEN, '')
+        translations.append(translation)
+
+    except:
+        translation = ''
+        translations.append(translation)
+        
+
+
+    print(translation)
+```
 
-[More Information Needed]
 
 ## 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. -->
+We used the Sprotin parallel corpus for **English–Faroese** translation: [barbaroo/Sprotin_parallel](https://huggingface.co/datasets/barbaroo/Sprotin_parallel). 
 
-[More Information Needed]
 
 ### 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]
 
-[More Information Needed]
-
+- **Tokenization**: We used the tokenizer from the base model `AI-Sweden-Models/gpt-sw3-1.3b`.
+- The Alpaca prompt format was used, with Instruction, Input and Response. 
 
 #### Training Hyperparameters
+ 
+- **Epochs**: **3** total, with an **early stopping** criterion monitoring validation loss.  
+- **Batch Size**: **2, with 4 Gradient accumulation steps**  
+- **Learning Rate**: **2e-4** 
+- **Optimizer**: **AdamW** with a linear learning-rate scheduler and warm-up.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+---
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
+- The model was evaluated on the **[FLORES-200]** benchmark,  of ~1012 English–Faroese pairs.  
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+#### Metrics and Results
 
-### Results
+- **BLEU**: **[0.179]** 
+- **chrF**: **[49.2]**
+- **BERTScore f1**: **[0.947]**
 
-[More Information Needed]
+Human evaluation was also performed (see paper)
 
-#### Summary
 
+## Citation []
 
+[COMING SOON]
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
+---
+## Framework versions 
 
 - PEFT 0.13.0