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.

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README.md +315 -383

README.md CHANGED Viewed

@@ -1,384 +1,316 @@
----
-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
-widget:
-- text: "<<<Query>>>\nHow many languages has LB-Reranker been trained on?\n\n\n<<<Context>>>\nLB-Reranker has been trained on more than 95 languages."
-  example_title: Positive example (7/7)
-- text: "<<<Query>>>\nHow many languages has LB-Reranker been trained on?\n\n\n<<<Context>>>\nAA-Reranker is applicable to a broad range of use cases."
-  example_title: Negative example (2/7)
----
-# 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 vLLM, LMDeploy, and OpenAI (hosted for free on Huggingface):
-<ul>
-  <li><b>vLLM</b>
-Install [vLLM](https://github.com/vllm-project/vllm/) using `pip install vllm`.
-<details open>
-  <summary>Show vLLM code</summary>
-```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_inference_conversation(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_inference_conversation(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]
-```
-</details></li>
-  <li><b>LMDeploy</b>
-Install [LMDeploy](https://github.com/InternLM/lmdeploy) using `pip install lmdeploy`.
-<details>
-  <summary>Show LMDeploy code</summary>
-```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_inference_conversation(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_inference_conversation(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]
-```
-</details></li>
-  <li><b>OpenAI (Hosted on Huggingface)</b>
-Install [openai](https://github.com/openai/openai-python) using `pip install openai`.
-<details>
-  <summary>Show OpenAI + Huggingface Inference code</summary>
-```python
-from openai import OpenAI
-import numpy as np
-from multiprocessing import Pool
-from tqdm.auto import tqdm
-client = OpenAI(
-	base_url="https://api-inference.huggingface.co/v1/",
-	api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Change this to an access token from https://huggingface.co/settings/tokens
-)
-def make_reranker_input(t, q):
-    return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
-def make_reranker_inference_conversation(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_reranker_score(context_question_tuple):
-    question, context = context_question_tuple
-    messages = make_reranker_inference_conversation(context, question)
-    completion = client.chat.completions.create(
-        model="lightblue/lb-reranker-0.5B-v1.0",
-        messages=messages,
-        max_tokens=1,
-        temperature=0.0,
-        logprobs=True,
-        top_logprobs=5, # Max allowed by the openai API as top_n_tokens must be >= 0 and <= 5. If this gets changed, fix to > 7.
-    )
-    logprobs = completion.choices[0].logprobs.content[0].top_logprobs
-    calculated_score = sum([int(x.token) * np.exp(x.logprob) for x in logprobs])
-    return calculated_score
-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)."),
-]
-with Pool(processes=16) as p: # Allows for parallel processing
-    expected_vals = list(tqdm(p.imap(get_reranker_score, query_texts), total=len(query_texts)))
-print(expected_vals)
-# [6.64866580, 1.85144404, 1.010719508]
-```
-</details></li>
-</ul>
-# 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
+widget:
+- text: '<<<Query>>>
+    How many languages has LB-Reranker been trained on?
+    <<<Context>>>
+    LB-Reranker has been trained on more than 95 languages.'
+  example_title: Positive example (7/7)
+- text: '<<<Query>>>
+    How many languages has LB-Reranker been trained on?
+    <<<Context>>>
+    AA-Reranker is applicable to a broad range of use cases.'
+  example_title: Negative example (2/7)
+---
+# 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 vLLM, LMDeploy, and OpenAI (hosted for free on Huggingface):
+<ul>
+  <li><b>vLLM</b>
+Install [vLLM](https://github.com/vllm-project/vllm/) using `pip install vllm`.
+<details open>
+  <summary>Show vLLM code</summary>
+```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_inference_conversation(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_inference_conversation(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]
+```
+</details></li>
+  <li><b>LMDeploy</b>
+Install [LMDeploy](https://github.com/InternLM/lmdeploy) using `pip install lmdeploy`.
+<details>
+  <summary>Show LMDeploy code</summary>
+```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_inference_conversation(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_inference_conversation(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]
+```
+</details></li>
+  <li><b>OpenAI (Hosted on Huggingface)</b>
+Install [openai](https://github.com/openai/openai-python) using `pip install openai`.
+<details>
+  <summary>Show OpenAI + Huggingface Inference code</summary>
+```python
+from openai import OpenAI
+import numpy as np
+from multiprocessing import Pool
+from tqdm.auto import tqdm
+client = OpenAI(
+	base_url="https://api-inference.huggingface.co/v1/",
+	api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Change this to an access token from https://huggingface.co/settings/tokens
+)
+def make_reranker_input(t, q):
+    return f"<<<Query>>>\n{q}\n\n<<<Context>>>\n{t}"
+def make_reranker_inference_conversation(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_reranker_score(context_question_tuple):
+    question, context = context_question_tuple
+    messages = make_reranker_inference_conversation(context, question)
+    completion = client.chat.completions.create(
+        model="lightblue/lb-reranker-0.5B-v1.0",
+        messages=messages,
+        max_tokens=1,
+        temperature=0.0,
+        logprobs=True,
+        top_logprobs=5, # Max allowed by the openai API as top_n_tokens must be >= 0 and <= 5. If this gets changed, fix to > 7.
+    )
+    logprobs = completion.choices[0].logprobs.content[0].top_logprobs
+    calculated_score = sum([int(x.token) * np.exp(x.logprob) for x in logprobs])
+    return calculated_score
+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)."),
+]
+with Pool(processes=16) as p: # Allows for parallel processing
+    expected_vals = list(tqdm(p.imap(get_reranker_score, query_texts), total=len(query_texts)))
+print(expected_vals)
+# [6.64866580, 1.85144404, 1.010719508]
+```
+</details></li>
+</ul>
+# 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