Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,258 +1,171 @@
----
-library_name: transformers
-license: apache-2.0
-language:
-- multilingual
-- af
-- am
-- ar
-- as
-- azb
-- be
-- bg
-- bm
-- bn
-- bo
-- bs
-- ca
-- ceb
-- cs
-- cy
-- da
-- de
-- du
-- el
-- en
-- eo
-- es
-- et
-- eu
-- fa
-- fi
-- fr
-- ga
-- gd
-- gl
-- ha
-- hi
-- hr
-- ht
-- hu
-- id
-- ig
-- is
-- it
-- iw
-- ja
-- jv
-- ka
-- ki
-- kk
-- km
-- ko
-- la
-- lb
-- ln
-- lo
-- lt
-- lv
-- mi
-- mr
-- ms
-- mt
-- my
-- 'no'
-- oc
-- pa
-- pl
-- pt
-- qu
-- ro
-- ru
-- sa
-- sc
-- sd
-- sg
-- sk
-- sl
-- sm
-- so
-- sq
-- sr
-- ss
-- sv
-- sw
-- ta
-- te
-- th
-- ti
-- tl
-- tn
-- tpi
-- tr
-- ts
-- tw
-- uk
-- ur
-- uz
-- vi
-- war
-- wo
-- xh
-- yo
-- zh
-- zu
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-- timm/ViT-SO400M-14-SigLIP-384
-pipeline_tag: image-text-to-text
----
-
-# Centurio Qwen
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-- **Model type:** Centurio is an open-source multilingual large vision-language model.
-- **Training Data:** COMING SOON 
-- **Languages:** The model was trained with the following 100 languages: `af, am, ar, ar-eg, as, azb, be, bg, bm, bn, bo, bs, ca, ceb, cs, cy, da, de, du, el, en, eo, es, et, eu, fa, fi, fr, ga, gd, gl, ha, hi, hr, ht, hu, id, ig, is, it, iw, ja, jv, ka, ki, kk, km, ko, la, lb, ln, lo, lt, lv, mi, mr, ms, mt, my, no, oc, pa, pl, pt, qu, ro, ru, sa, sc, sd, sg, sk, sl, sm, so, sq, sr, ss, sv, sw, ta, te, th, ti, tl, tn, tpi, tr, ts, tw, uk, ur, uz, vi, war, wo, xh, yo, zh, zu
-`
-- **License:** This work is released under the Apache 2.0 license. 
-
-### Model Sources 
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [gregor-ge.github.io/Centurio](https://gregor-ge.github.io/Centurio)
-- **Paper:** [arXiv](https://arxiv.org/abs/2501.)
-
-## 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. -->
-
-The model can be used directly through the `transformers` library with our custom code. 
-
-```python
-from transformers import AutoModelForCausalLM, AutoProcessor
-import timm
-from PIL import Image    
-import requests
-
-url = "https://upload.wikimedia.org/wikipedia/commons/b/bd/Golden_Retriever_Dukedestiny01_drvd.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
-model_name = "WueNLP/centurio_qwen"
-
-processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
-
-## Appearance of images in the prompt are indicates with '<image_placeholder>'!
-prompt = "<image_placeholder>\nBriefly describe the image in German."
-
-messages = [
-    {"role": "system", "content": "You are a helpful assistant."},  # This is the system prompt used during our training.
-    {"role": "user", "content": prompt}
-]
-
-text = processor.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    trust_remote_code=True
-)
-
-model_inputs = processor(text=[text], images=[image] return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=128
-)
-
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-```
-
-#### Multiple Images
-We natively support multi-image inputs. You only have to 1) include more `<image_placeholder>` while 2) passing all images of the *entire batch* as a flat list:
-
-```python
-[...]
-# Variables reused from above.
-
-processor.tokenizer.padding_side = "left" # default is 'right' but has to be 'left' for batched generation to work correctly!
-
-image_multi_1, image_multi_2 = [...] # prepare additional images
-
-prompt_multi = "What is the difference between the following images?\n<image_placeholder><image_placeholder>\nAnswer in German."
-
-messages_multi = [
-    {"role": "system", "content": "You are a helpful assistant."}, 
-    {"role": "user", "content": prompt_multi}
-]
-
-text_multi = processor.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-
-model_inputs = processor(text=[text, text_multi], images=[image, image_multi_1, image_multi_2] return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=128
-)
-
-[...]
-
-```
-
-
-
-
-## Bias, Risks, and Limitations
-
-- General biases, risks, and limitations of large vision-language models like hallucinations or biases from training data apply.
-- This is a research project and *not* recommended for production use.
-- Multilingual: Performance and generation quality can differ widely between languages. 
-- OCR: Model struggles both with small text and writing in non-Latin scripts.
-
-
-## Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-```
-@article{centurio2025,
-  author       = {Gregor Geigle and
-                  Florian Schneider and
-                  Carolin Holtermann and
-                  Chris Biemann and
-                  Radu Timofte and
-                  Anne Lauscher and
-                  Goran Glava\v{s}},
-  title        = {Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model},
-  journal      = {arXiv},
-  volume       = {abs/2501.05122},
-  year         = {2025},
-  url          = {https://arxiv.org/abs/2501.05122},
-  eprinttype    = {arXiv},
-  eprint       = {2501.05122},
-}
+---
+library_name: transformers
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+- timm/ViT-SO400M-14-SigLIP-384
+pipeline_tag: image-text-to-text
+---
+
+# Centurio Qwen
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Model type:** Centurio is an open-source multilingual large vision-language model.
+- **Training Data:** COMING SOON 
+- **Languages:** The model was trained with the following 100 languages: `af, am, ar, ar-eg, as, azb, be, bg, bm, bn, bo, bs, ca, ceb, cs, cy, da, de, du, el, en, eo, es, et, eu, fa, fi, fr, ga, gd, gl, ha, hi, hr, ht, hu, id, ig, is, it, iw, ja, jv, ka, ki, kk, km, ko, la, lb, ln, lo, lt, lv, mi, mr, ms, mt, my, no, oc, pa, pl, pt, qu, ro, ru, sa, sc, sd, sg, sk, sl, sm, so, sq, sr, ss, sv, sw, ta, te, th, ti, tl, tn, tpi, tr, ts, tw, uk, ur, uz, vi, war, wo, xh, yo, zh, zu
+`
+- **License:** This work is released under the Apache 2.0 license. 
+
+### Model Sources 
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [gregor-ge.github.io/Centurio](https://gregor-ge.github.io/Centurio)
+- **Paper:** [arXiv](https://arxiv.org/abs/2501.)
+
+## 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. -->
+
+The model can be used directly through the `transformers` library with our custom code. 
+
+```python
+from transformers import AutoModelForCausalLM, AutoProcessor
+import timm
+from PIL import Image    
+import requests
+
+url = "https://upload.wikimedia.org/wikipedia/commons/b/bd/Golden_Retriever_Dukedestiny01_drvd.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+model_name = "WueNLP/centurio_qwen"
+
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+## Appearance of images in the prompt are indicates with '<image_placeholder>'!
+prompt = "<image_placeholder>\nBriefly describe the image in German."
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},  # This is the system prompt used during our training.
+    {"role": "user", "content": prompt}
+]
+
+text = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
+
+model_inputs = processor(text=[text], images=[image] return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=128
+)
+
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+```
+
+#### Multiple Images
+We natively support multi-image inputs. You only have to 1) include more `<image_placeholder>` while 2) passing all images of the *entire batch* as a flat list:
+
+```python
+[...]
+# Variables reused from above.
+
+processor.tokenizer.padding_side = "left" # default is 'right' but has to be 'left' for batched generation to work correctly!
+
+image_multi_1, image_multi_2 = [...] # prepare additional images
+
+prompt_multi = "What is the difference between the following images?\n<image_placeholder><image_placeholder>\nAnswer in German."
+
+messages_multi = [
+    {"role": "system", "content": "You are a helpful assistant."}, 
+    {"role": "user", "content": prompt_multi}
+]
+
+text_multi = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = processor(text=[text, text_multi], images=[image, image_multi_1, image_multi_2] return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=128
+)
+
+[...]
+
+```
+
+
+
+
+## Bias, Risks, and Limitations
+
+- General biases, risks, and limitations of large vision-language models like hallucinations or biases from training data apply.
+- This is a research project and *not* recommended for production use.
+- Multilingual: Performance and generation quality can differ widely between languages. 
+- OCR: Model struggles both with small text and writing in non-Latin scripts.
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```
+@article{centurio2025,
+  author       = {Gregor Geigle and
+                  Florian Schneider and
+                  Carolin Holtermann and
+                  Chris Biemann and
+                  Radu Timofte and
+                  Anne Lauscher and
+                  Goran Glava\v{s}},
+  title        = {Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model},
+  journal      = {arXiv},
+  volume       = {abs/2501.05122},
+  year         = {2025},
+  url          = {https://arxiv.org/abs/2501.05122},
+  eprinttype    = {arXiv},
+  eprint       = {2501.05122},
+}
 ```