Improve language tag

README.md

@@ -1,315 +1,229 @@
-
----
-
-library_name: transformers
-license: apache-2.0
-language:
-- en
-- zh
-- es
-- de
-- ar
-- ru
-- ja
-- ko
-- hi
-- sk
-- vi
-- tr
-- fi
-- id
-- fa
-- 'no'
-- th
-- sv
-- pt
-- da
-- bn
-- te
-- ro
-- it
-- fr
-- nl
-- sw
-- pl
-- hu
-- cs
-- el
-- uk
-- mr
-- ta
-- tl
-- bg
-- lt
-- ur
-- he
-- gu
-- kn
-- am
-- kk
-- hr
-- uz
-- jv
-- ca
-- az
-- ms
-- sr
-- sl
-- yo
-- lv
-- is
-- ha
-- ka
-- et
-- bs
-- hy
-- ml
-- pa
-- mt
-- km
-- sq
-- or
-- as
-- my
-- mn
-- af
-- be
-- ga
-- mk
-- cy
-- gl
-- ceb
-- la
-- yi
-- lb
-- tg
-- gd
-- ne
-- ps
-- eu
-- ky
-- ku
-- si
-- ht
-- eo
-- lo
-- fy
-- sd
-- mg
-- so
-- ckb
-- su
-- nn
-datasets:
-- lightblue/reranker_continuous_filt_max7_train
-base_model:
-- Qwen/Qwen2.5-0.5B-Instruct
-pipeline_tag: text-generation
-tags:
-- reranker
-
----
-
-[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
-
-
-# QuantFactory/lb-reranker-0.5B-v1.0-GGUF
-This is quantized version of [lightblue/lb-reranker-0.5B-v1.0](https://huggingface.co/lightblue/lb-reranker-0.5B-v1.0) created using llama.cpp
-
-# Original Model Card
-
-
-# LB Reranker v1.0
-
-<div style="width: 100%; height: 160px; 
-            display: flex; align-items: center; 
-            justify-content: center; 
-            border: 8px solid black; 
-            font-size: 120px; font-weight: bold; 
-            text-align: center;
-            color: #438db8; 
-            font-family: 'Helvetica Neue', sans-serif;">
-  LBR
-</div>
-
-The LB Reranker has been trained to determine the relatedness of a given query to a piece of text, therefore allowing it to be used as a ranker or reranker in various retrieval-based tasks.
-
-This model is fine-tuned from a [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model checkpoint and was trained for roughly 5.5 hours using the 8 x L20 instance ([ecs.gn8is-8x.32xlarge](https://www.alibabacloud.com/help/en/ecs/user-guide/gpu-accelerated-compute-optimized-and-vgpu-accelerated-instance-families-1)) on [Alibaba Cloud](https://www.alibabacloud.com/).
-
-The training data for this model can be found at [lightblue/reranker_continuous_filt_max7_train](https://huggingface.co/datasets/lightblue/reranker_continuous_filt_max7_train) and the code for generating this data as well as running the training of the model can be found on [our Github repo](https://github.com/lightblue-tech/lb-reranker).
-
-Trained on data in over 95 languages, this model is applicable to a broad range of use cases.
-
-This model has three main benefits over comparable rerankers.
-1. It has shown slightly higher performance on evaluation benchmarks.
-2. It has been trained on more languages than any previous model.
-3. It is a simple Causal LM model trained to output a string between "1" and "7".
-
-This last point means that this model can be used natively with many widely available inference packages, including vLLM and LMDeploy.
-This in turns allows our reranker to benefit from improvements to inference as and when these packages release them.
-
-Update: We have also found that this model works pretty well as a code snippet reranker too (P@1 of 96%)! See our [Colab](https://colab.research.google.com/drive/1ABL1xaarekLIlVJKbniYhXgYu6ZNwfBm?usp=sharing) for more details.
-
-# How to use
-
-The model was trained to expect an input such as:
-
-```
-<<<Query>>>
-{your_query_here}
-
-<<<Context>>>
-{your_context_here}
-```
-
-And to output a string of a number between 1-7.
-
-In order to make a continuous score that can be used for reranking query-context pairs (i.e. a method with few ties), we calculate the expectation value of the scores.
-
-We include scripts to do this in both vLLM and LMDeploy:
-
-#### vLLM
-
-Install [vLLM](https://github.com/vllm-project/vllm/) using `pip install vllm`.
-
-```python
-from vllm import LLM, SamplingParams
-import numpy as np
-
-def make_reranker_input(t, q):
-    return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
-
-def make_reranker_training_datum(context, question):
-    system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related."
-
-    return [
-        {"role": "system", "content": system_message},
-        {"role": "user", "content": make_reranker_input(context, question)},
-    ]
-
-def get_prob(logprob_dict, tok_id):
-    return np.exp(logprob_dict[tok_id].logprob) if tok_id in logprob_dict.keys() else 0
-
-llm = LLM("lightblue/lb-reranker-v1.0")
-sampling_params = SamplingParams(temperature=0.0, logprobs=14, max_tokens=1)
-tok = llm.llm_engine.tokenizer.tokenizer
-idx_tokens = [tok.encode(str(i))[0] for i in range(1, 8)]
-
-query_texts = [
-    ("What is the scientific name of apples?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
-    ("What is the Chinese word for 'apple'?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
-    ("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
-]
-
-chats = [make_reranker_training_datum(c, q) for q, c in query_texts]
-responses = llm.chat(chats, sampling_params)
-probs = np.array([[get_prob(r.outputs[0].logprobs[0], y) for y in idx_tokens] for r in responses])
-
-N = probs.shape[1]
-M = probs.shape[0]
-idxs = np.tile(np.arange(1, N + 1), M).reshape(M, N)
-
-expected_vals = (probs * idxs).sum(axis=1)
-print(expected_vals)
-# [6.66570732 1.86686378 1.01102923]
-```
-
-#### LMDeploy
-
-Install [LMDeploy](https://github.com/InternLM/lmdeploy) using `pip install lmdeploy`.
-
-```python
-# Un-comment this if running in a Jupyter notebook, Colab etc.
-# import nest_asyncio
-# nest_asyncio.apply()
-
-from lmdeploy import GenerationConfig, ChatTemplateConfig, pipeline
-import numpy as np
-
-def make_reranker_input(t, q):
-    return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
-
-def make_reranker_training_datum(context, question):
-    system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related."
-
-    return [
-        {"role": "system", "content": system_message},
-        {"role": "user", "content": make_reranker_input(context, question)},
-    ]
-
-def get_prob(logprob_dict, tok_id):
-    return np.exp(logprob_dict[tok_id]) if tok_id in logprob_dict.keys() else 0
-
-pipe = pipeline(
-    "lightblue/lb-reranker-v1.0",
-    chat_template_config=ChatTemplateConfig(
-                    model_name='qwen2d5',
-                    capability='chat'
-    )
-)
-tok = pipe.tokenizer.model
-idx_tokens = [tok.encode(str(i))[0] for i in range(1, 8)]
-
-query_texts = [
-    ("What is the scientific name of apples?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
-    ("What is the Chinese word for 'apple'?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
-    ("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
-]
-
-chats = [make_reranker_training_datum(c, q) for q, c in query_texts]
-responses = pipe(
-    chats, 
-    gen_config=GenerationConfig(temperature=1.0, logprobs=14, max_new_tokens=1, do_sample=True)
-)
-probs = np.array([[get_prob(r.logprobs[0], y) for y in idx_tokens] for r in responses])
-
-N = probs.shape[1]
-M = probs.shape[0]
-idxs = np.tile(np.arange(1, N + 1), M).reshape(M, N)
-
-expected_vals = (probs * idxs).sum(axis=1)
-print(expected_vals)
-# [6.66415229 1.84342025 1.01133205]
-```
-
-# Evaluation
-
-We perform an evaluation on 9 datasets from the [BEIR benchmark](https://github.com/beir-cellar/beir) that none of the evaluated models have been trained upon (to our knowledge).
-
-* Arguana
-* Dbpedia-entity
-* Fiqa
-* NFcorpus
-* Scidocs
-* Scifact
-* Trec-covid-v2
-* Vihealthqa
-* Webis-touche2020
-
-We evaluate on a subset of all queries (the first 250) to save evaluation time.
-
-We find that our model performs similarly or better than many of the state-of-the-art reranker models in our evaluation, without compromising on inference speed.
-
-We make our evaluation code and results available [on our Github](https://github.com/lightblue-tech/lb-reranker/blob/main/run_bier.ipynb).
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/xkNzCABFUmU7UmDXUduiz.png)
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/P-XCA3TGHqDSX8k6c4hCE.png)
-
-As we can see, this reranker attains greater IR evaluation metrics compared to the two benchmarks we include for all positions apart from @1.
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/puhhWseBOcIyOEdW4L-B0.png)
-
-We also show that our model is, on average, faster than the BGE reranker v2.
-
-# License
-
-We share this model under an Apache 2.0 license.
-
-# Developed by
-
-<a href="https://www.lightblue-tech.com">
-<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/>
-</a>
-
-This model was trained by Peter Devine ([ptrdvn](https://huggingface.co/ptrdvn)) for Lightblue
+---
+library_name: transformers
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- lightblue/reranker_continuous_filt_max7_train
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+tags:
+- reranker
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/lb-reranker-0.5B-v1.0-GGUF
+This is quantized version of [lightblue/lb-reranker-0.5B-v1.0](https://huggingface.co/lightblue/lb-reranker-0.5B-v1.0) created using llama.cpp
+
+# Original Model Card
+
+
+# LB Reranker v1.0
+
+<div style="width: 100%; height: 160px; 
+            display: flex; align-items: center; 
+            justify-content: center; 
+            border: 8px solid black; 
+            font-size: 120px; font-weight: bold; 
+            text-align: center;
+            color: #438db8; 
+            font-family: 'Helvetica Neue', sans-serif;">
+  LBR
+</div>
+
+The LB Reranker has been trained to determine the relatedness of a given query to a piece of text, therefore allowing it to be used as a ranker or reranker in various retrieval-based tasks.
+
+This model is fine-tuned from a [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model checkpoint and was trained for roughly 5.5 hours using the 8 x L20 instance ([ecs.gn8is-8x.32xlarge](https://www.alibabacloud.com/help/en/ecs/user-guide/gpu-accelerated-compute-optimized-and-vgpu-accelerated-instance-families-1)) on [Alibaba Cloud](https://www.alibabacloud.com/).
+
+The training data for this model can be found at [lightblue/reranker_continuous_filt_max7_train](https://huggingface.co/datasets/lightblue/reranker_continuous_filt_max7_train) and the code for generating this data as well as running the training of the model can be found on [our Github repo](https://github.com/lightblue-tech/lb-reranker).
+
+Trained on data in over 95 languages, this model is applicable to a broad range of use cases.
+
+This model has three main benefits over comparable rerankers.
+1. It has shown slightly higher performance on evaluation benchmarks.
+2. It has been trained on more languages than any previous model.
+3. It is a simple Causal LM model trained to output a string between "1" and "7".
+
+This last point means that this model can be used natively with many widely available inference packages, including vLLM and LMDeploy.
+This in turns allows our reranker to benefit from improvements to inference as and when these packages release them.
+
+Update: We have also found that this model works pretty well as a code snippet reranker too (P@1 of 96%)! See our [Colab](https://colab.research.google.com/drive/1ABL1xaarekLIlVJKbniYhXgYu6ZNwfBm?usp=sharing) for more details.
+
+# How to use
+
+The model was trained to expect an input such as:
+
+```
+<<<Query>>>
+{your_query_here}
+
+<<<Context>>>
+{your_context_here}
+```
+
+And to output a string of a number between 1-7.
+
+In order to make a continuous score that can be used for reranking query-context pairs (i.e. a method with few ties), we calculate the expectation value of the scores.
+
+We include scripts to do this in both vLLM and LMDeploy:
+
+#### vLLM
+
+Install [vLLM](https://github.com/vllm-project/vllm/) using `pip install vllm`.
+
+```python
+from vllm import LLM, SamplingParams
+import numpy as np
+
+def make_reranker_input(t, q):
+    return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
+
+def make_reranker_training_datum(context, question):
+    system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related."
+
+    return [
+        {"role": "system", "content": system_message},
+        {"role": "user", "content": make_reranker_input(context, question)},
+    ]
+
+def get_prob(logprob_dict, tok_id):
+    return np.exp(logprob_dict[tok_id].logprob) if tok_id in logprob_dict.keys() else 0
+
+llm = LLM("lightblue/lb-reranker-v1.0")
+sampling_params = SamplingParams(temperature=0.0, logprobs=14, max_tokens=1)
+tok = llm.llm_engine.tokenizer.tokenizer
+idx_tokens = [tok.encode(str(i))[0] for i in range(1, 8)]
+
+query_texts = [
+    ("What is the scientific name of apples?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
+    ("What is the Chinese word for 'apple'?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
+    ("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
+]
+
+chats = [make_reranker_training_datum(c, q) for q, c in query_texts]
+responses = llm.chat(chats, sampling_params)
+probs = np.array([[get_prob(r.outputs[0].logprobs[0], y) for y in idx_tokens] for r in responses])
+
+N = probs.shape[1]
+M = probs.shape[0]
+idxs = np.tile(np.arange(1, N + 1), M).reshape(M, N)
+
+expected_vals = (probs * idxs).sum(axis=1)
+print(expected_vals)
+# [6.66570732 1.86686378 1.01102923]
+```
+
+#### LMDeploy
+
+Install [LMDeploy](https://github.com/InternLM/lmdeploy) using `pip install lmdeploy`.
+
+```python
+# Un-comment this if running in a Jupyter notebook, Colab etc.
+# import nest_asyncio
+# nest_asyncio.apply()
+
+from lmdeploy import GenerationConfig, ChatTemplateConfig, pipeline
+import numpy as np
+
+def make_reranker_input(t, q):
+    return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
+
+def make_reranker_training_datum(context, question):
+    system_message = "Given a query and a piece of text, output a score of 1-7 based on how related the query is to the text. 1 means least related and 7 is most related."
+
+    return [
+        {"role": "system", "content": system_message},
+        {"role": "user", "content": make_reranker_input(context, question)},
+    ]
+
+def get_prob(logprob_dict, tok_id):
+    return np.exp(logprob_dict[tok_id]) if tok_id in logprob_dict.keys() else 0
+
+pipe = pipeline(
+    "lightblue/lb-reranker-v1.0",
+    chat_template_config=ChatTemplateConfig(
+                    model_name='qwen2d5',
+                    capability='chat'
+    )
+)
+tok = pipe.tokenizer.model
+idx_tokens = [tok.encode(str(i))[0] for i in range(1, 8)]
+
+query_texts = [
+    ("What is the scientific name of apples?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
+    ("What is the Chinese word for 'apple'?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
+    ("What is the square root of 999?", "An apple is a round, edible fruit produced by an apple tree (Malus spp., among them the domestic or orchard apple; Malus domestica)."),
+]
+
+chats = [make_reranker_training_datum(c, q) for q, c in query_texts]
+responses = pipe(
+    chats, 
+    gen_config=GenerationConfig(temperature=1.0, logprobs=14, max_new_tokens=1, do_sample=True)
+)
+probs = np.array([[get_prob(r.logprobs[0], y) for y in idx_tokens] for r in responses])
+
+N = probs.shape[1]
+M = probs.shape[0]
+idxs = np.tile(np.arange(1, N + 1), M).reshape(M, N)
+
+expected_vals = (probs * idxs).sum(axis=1)
+print(expected_vals)
+# [6.66415229 1.84342025 1.01133205]
+```
+
+# Evaluation
+
+We perform an evaluation on 9 datasets from the [BEIR benchmark](https://github.com/beir-cellar/beir) that none of the evaluated models have been trained upon (to our knowledge).
+
+* Arguana
+* Dbpedia-entity
+* Fiqa
+* NFcorpus
+* Scidocs
+* Scifact
+* Trec-covid-v2
+* Vihealthqa
+* Webis-touche2020
+
+We evaluate on a subset of all queries (the first 250) to save evaluation time.
+
+We find that our model performs similarly or better than many of the state-of-the-art reranker models in our evaluation, without compromising on inference speed.
+
+We make our evaluation code and results available [on our Github](https://github.com/lightblue-tech/lb-reranker/blob/main/run_bier.ipynb).
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/xkNzCABFUmU7UmDXUduiz.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/P-XCA3TGHqDSX8k6c4hCE.png)
+
+As we can see, this reranker attains greater IR evaluation metrics compared to the two benchmarks we include for all positions apart from @1.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/puhhWseBOcIyOEdW4L-B0.png)
+
+We also show that our model is, on average, faster than the BGE reranker v2.
+
+# License
+
+We share this model under an Apache 2.0 license.
+
+# Developed by
+
+<a href="https://www.lightblue-tech.com">
+<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/>
+</a>
+
+This model was trained by Peter Devine ([ptrdvn](https://huggingface.co/ptrdvn)) for Lightblue