Update README.md

README.md

@@ -1,6 +1,22 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- text-summarization
+- text-generation
+- clinical-report-summarization
+- document-summarization
+license: mit
+language:
+- en
+- fr
+- pt
+- es
+metrics:
+- bertscore
+- rouge
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
 ---
 
 # Model Card for Model ID
@@ -8,7 +24,6 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
 
 
-
 ## Model Details
 
 ### Model Description
@@ -35,19 +50,42 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## 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]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+We use [bulk-chain](https://github.com/nicolay-r/bulk-chain) for inference with the Qwen2 provider based on `transformers` **pipelines API**.
+
+**Provider**: https://github.com/nicolay-r/nlp-thirdgate/blob/9e46629792e9a53871710884f7b9e2fe42666aa7/llm/transformers_qwen2.py
+
+```python
+from bulk_chain.api import iter_content
+from bulk_chain.core.utils import dynamic_init
+
+content_it = iter_content(
+        schema={"schema": [
+            {"prompt": "Summarize: {input}", "out": "summary"}]
+        },
+        llm=dynamic_init(class_filepath="providers/huggingface_qwen.py", class_name="Qwen2")(
+            api_token="YOUR_HF_API_KEY_GOES_HERE",
+            model_name="nicolay-r/qwen25-05b-multiclinsum-standard",
+            temp=0.1,
+            use_bf16=True,
+            max_new_tokens=args.max_tokens,
+            device=args.device
+        ),
+        infer_mode="batch",
+        batch_size=4,
+        return_mode="record",
+        // INPUT TEXTS:
+        input_dicts_it=[
+           {"input": "A patient 62 years old with ..."}
+        ],
+    )
+
+for record in content_it:
+  # here is the result dictionary that includes summary.
+  print(record["summary"])
+```
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
@@ -102,7 +140,8 @@ Use the code below to get started with the model.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/u2cKHWqolMT8LJvI6l5mT.png)
+
 
 ### Testing Data, Factors & Metrics
 
@@ -132,24 +171,6 @@ Use the code below to get started with the model.
 
 
 
-## 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