Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 language:
-- fr
+- en
 license: cc-by-nc-sa-4.0
 pipeline_tag: text-generation
 base_model: tiiuae/falcon-7b
@@ -9,23 +9,23 @@ tags:
 - conversational
 widget:
   - text: |-
-      - Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-      - Bonjour Camille,
+      - Hello Alice, what are you cooking for us today?
+      - Hello Bob,
     example_title: Request for a recipe
     group: Dash
   - text: |-
-      [Intervenant 1:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-      [Intervenant 2:] Bonjour Camille,
+      [Intervenant 1:] Hello Alice, what are you cooking for us today?
+      [Intervenant 2:] Hello Bob,
     example_title: Request for a recipe
     group: Intervenant
   - text: |-
-      [Camille:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-      [Dominique:] Bonjour Camille,
+      [Camille:] Hello Alice, what are you cooking for us today?
+      [Dominique:] Hello Bob,
     example_title: Request for a recipe
     group: FirstName
   - text: |-
-      [Camille Durand:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-      [Dominique Petit:] Bonjour Camille,
+      [Bob Brown:] Hello Alice, what are you cooking for us today?
+      [Alice Green:] Hello Bob,
     example_title: Request for a recipe
     group: Named
 inference:
@@ -35,21 +35,21 @@ inference:
         top_k: 10
 ---
 
-# Claire-7B-0.1
+# Claire-7B-EN-0.1
 
 **Claire-7B-0.1 is a 7B parameter causal decoder-only model built by [LINAGORA](https://labs.linagora.com/) and [OpenLLM-France](https://github.com/OpenLLM-France)**
-**adapted from [Falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on French conversational data.**
+**adapted from [Falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on English conversational data.**
 
-Quantized versions in GGUF format can be found in [TheBloke/Claire-7B-0.1-GGUF](https://huggingface.co/TheBloke/Claire-7B-0.1-GGUF).
+<!-- Quantized versions in GGUF format can be found in [TheBloke/Claire-7B-0.1-GGUF](https://huggingface.co/TheBloke/Claire-7B-0.1-GGUF). -->
 
-Claire-7B-0.1 is a pretrained language model designed to be attuned to the dynamics of linguistic interactions in dialogue. Without further training, its expected use is to generate continuations of dialogues. Its main purpose is to serve as a base model for fine-tuning on dialogue generation (e.g., chat) and dialogue understanding (e.g., meeting summarization) tasks. Please note that due to its training, the model is prone to generate dialogues with disfluencies and other constructions common to spoken language.
+Claire-7B-EN-0.1 is a pretrained language model designed to be attuned to the dynamics of linguistic interactions in dialogue. Without further training, its expected use is to generate continuations of dialogues. Its main purpose is to serve as a base model for fine-tuning on dialogue generation (e.g., chat) and dialogue understanding (e.g., meeting summarization) tasks. Please note that due to its training, the model is prone to generate dialogues with disfluencies and other constructions common to spoken language.
 
 * [Typical usage](#typical-usage)
   * [Typical prompts](#typical-prompts)
 * [Training Details](#training-details)
   * [Training Data](#training-data)
   * [Training Procedure](#training-procedure)
-* [Evaluation](#evaluation)
+<!-- * [Evaluation](#evaluation) -->
 * [License](#license)
 * [Acknowledgements](#acknowledgements)
 * [Contact](#contact)
@@ -61,7 +61,7 @@ Claire-7B-0.1 is a pretrained language model designed to be attuned to the dynam
 import transformers
 import torch
 
-model_name = "OpenLLM-France/Claire-7B-0.1"
+model_name = "OpenLLM-France/Claire-7B-EN-0.1"
 
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
@@ -80,8 +80,8 @@ generation_kwargs = dict(
 )
 
 prompt = """\
-- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-- Bonjour Camille,\
+- Hello Alice, what are you cooking for us today?
+- Hello Bob,\
 """
 completions = pipeline(prompt, **generation_kwargs)
 for completion in completions:
@@ -89,13 +89,15 @@ for completion in completions:
 ```
 This will print something like:
 ```
-- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-- Bonjour Camille, […] je vous prépare un plat de saison, une daube provençale.
-- Ah je ne connais pas cette recette.
-- C'est très facile à préparer, vous n'avez qu'à mettre de l'eau dans une marmite, y mettre de l'oignon émincé, des carottes coupées en petits morceaux, et vous allez mettre votre viande de bœuf coupé en petits morceaux également.
-- Je n'ai jamais cuisiné de viande de bœuf, mais c'est vrai que ça a l'air bien facile.
-- Vous n'avez plus qu'à laisser mijoter, et ensuite il sera temps de servir les clients.
-- Très bien.
+- Hello Alice, what are you cooking for us today?
+- Hello Bob, […] I'm going to make beef and vegetables.
+- That sounds great. What type of vegetables are you going to make?
+- I'm thinking of making a broccoli salad and steamed potatoes.
+- I love broccoli and potatoes, especially together. Do you plan to make a dressing or a mayo for the broccoli?
+- Yes, I have to make a dressing. How about some mayo for the potatoes?
+- I don't know if I like the sound of that, but go for it. You're the chef! I'll try some.
+- I'm sure you will.
+- I'll try some.
 ```
 
 You will need at least 6GB of VRAM to run inference using 4bit quantization (16GB of VRAM without 4bit quantization).
@@ -104,26 +106,26 @@ If you have trouble running this code, make sure you have recent versions of `to
 
 ### Typical prompts
 
-Claire-7B-0.1 was trained on diarized French conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:
+Claire-7B-EN-0.1 was trained on English conversations. During training, the dialogues were normalized in several formats. The possible formats for expected prompts are as follows:
 
 A monologue can be specified as a single line prompt (though keep in mind that Claire might still return a dialogue because of its training):
 ```python
-prompt = "Mesdames et messieurs les députés, chers collègues, bonsoir. Vous l'aurez peut-être remarqué, je cite rarement"
+prompt = "Ladies and gentlemen, welcome aboard the S.S. Anne! We will be leaving in"
 ```
 
 A dialogue between two speakers can be specified with one line per speech turn starting with a dash:
 ```python
 prompt = """\
-- Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
+- Hello Alice, what are you cooking for us today?
 - Bonjour Camille,\
 """
 ```
 
-A dialogue or multilogue (with two or more speakers) can be specified with lines that start with `[Intervenant X:]` where `X` is a number:
+A dialogue or multilogue (with two or more speakers) can be specified with lines that start with `[Speaker X:]` where `X` is a number:
 ```python
 prompt = """\
-[Intervenant 1:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-[Intervenant 2:] Bonjour Camille,\
+[Speaker 1:] Hello Alice, what are you cooking for us today?
+[Speaker 2:] Hello Bob,\
 """
 ```
 
@@ -131,8 +133,8 @@ A dialogue or multilogue with named speakers can be specified with lines that st
 where `SpeakerName` can be a first name, a first and a last name, a nickname, a title…
 ```python
 prompt = """\
-[Mme Camille Durand:] Bonjour Dominique, qu'allez-vous nous cuisiner aujourd'hui ?
-[Mr. Dominique Petit:] Bonjour Camille,\
+[Bob:] Hello Alice, what are you cooking for us today?
+[Alice:] Hello Bob,\
 """
 ```
 
@@ -140,40 +142,40 @@ prompt = """\
 
 ### Training Data
 
-The training dataset is available at [OpenLLM-France/Claire-Dialogue-French-0.1](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-French-0.1)
-and described in ["The Claire French Dialogue Dataset" (2023)](https://arxiv.org/abs/2311.16840).
+The training dataset is available at [OpenLLM-France/Claire-Dialogue-English-0.1](https://huggingface.co/datasets/OpenLLM-France/Claire-Dialogue-English-0.1)
+<!-- and described in ["The Claire French Dialogue Dataset" (2023)](https://arxiv.org/abs/2311.16840). -->
 
-Claire-7B-0.1 was tuned from Falcon-7b on the following data distribution:
+Claire-7B-EN-0.1 was tuned from Falcon-7b on the following data distribution:
 
 | **Data type**                 | **Words**  | **Training Sampling Weight** | **Sources**                                         |
 |-------------------------------|------------|------------------------------|-----------------------------------------------------|
-| Parliamentary Proceedings     | 135M       | 35%                          | Assemblée Nationale                                 |
-| Theatre                       |  16M       | 18%                          | Théâtre Classique, Théâtre Gratuit                  |
-| Interviews                    |   6.4M     | 29%                          | TCOF, CFPP, CFPB (ORFEO), ACSYNT, PFC, Valibel (ORFEO), ESLO|
-| Free Conversations            |   2.2M     | 10%                          | CRFP (ORFEO), OFROM (ORFEO), CID, Rhapsodie, ParisStories, PFC, CLAPI, C-ORAL-ROM (ORFEO), LinTO, ESLO |
-| Meetings                      |   1.2M     |  5%                          | SUMM-RE, LinTO, Réunions de travail (ORFEO)         |
-| Debates                       |   402k     | <2%                          | FREDSum, ESLO                                       |
-| Assistance                    |   159k     | <1%                          | Fleuron (ORFEO), Accueil UBS, OTG, ESLO             |
-| Presentation, Formal Address  |    86k     | <0.5%                        | Valibel (ORFEO), LinTO, ESLO                        |
+| Broadcast                     | 720M       | 43%                          | MediaSum                                            |
+| Parliamentary proceedings     |  56M       | 27%                          | Europarl                                            |
+| Assistance                    |  53M       | 13%                          | ReDial, OpenDialKG, ABCD, AirDialog, MULTIWOZ2_2, MulDoGO |
+| Misc                          |  10M       | 10%                          | British National Corpus (BNC)                       |
+| Spoken dialogue               |   4.7M     | 4.6%                         | Charlotte, Switchboard                              |
+| Meetings                      |   1.5M     | <2%                          | AMI, ICSI                                           |
+| Free Chat                     |   3.6M     | <1%                          | Chit-Chat, Daily Dialog                             |
+
 
 Training data was augmented with the following techniques:
 * varying the format used to indicate speech turns (dashes or [XXX:])
-* substituting [Intervenant X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name
+* substituting [Speaker X:] for [SpeakerName:] or vice versa, where [SpeakerName:] might be a real name or a randomly generated name
 * removing punctuation marks and/or casing (to prepare the model for transcripts produced by some Automatic Speech Recognition systems)
 
 Long conversations were truncated at a maximum of 2048 tokens. Where possible, they were split between speaker turns.
 
-While the model has been trained and evaluated only on French dialogues, it may be able to generate conversations in other languages from the original Falcon-7b training data.
+While the model has been trained and evaluated only on English dialogues, it may be able to generate conversations in other languages from the original Falcon-7b training data.
 
 
 ### Training Procedure 
 
 The training code is available at [https://github.com/OpenLLM-France/Lit-Claire](https://github.com/OpenLLM-France/Lit-Claire).
 
-Claire-7B-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
+Claire-7B-EN-0.1 is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
 See [Falcon-7b](https://huggingface.co/tiiuae/falcon-7b) for more details.
 
-Claire-7B-0.1 was trained on 1 A100 80GB GPU for about 50 GPU hours.
+Claire-7B-EN-0.1 was trained on 1 A100 80GB GPU for about 50 GPU hours.
 
 Hyperparameters were the following:
 | **Hyperparameter** | **Value**  |
@@ -188,9 +190,10 @@ Hyperparameters were the following:
 | Dropout            | 0.05       |
 | gradient clipping  | 1          |
 
+<!--
 ## Evaluation
 
-To evaluate Claire-7B-0.1’s ability to generate natural sounding, French conversations, we compared its responses to a variety of prompts with those of three other models:
+To evaluate Claire-7B-EN-0.1’s ability to generate natural sounding, French conversations, we compared its responses to a variety of prompts with those of three other models:
 * [Falcon-7b](https://huggingface.co/tiiuae/falcon-7b),
 * [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 
 * [Claire-Mistral-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-Mistral-7B-0.1) (a version of Mistral-7B-v0.1 adapted in the same fashion as Claire-7B-0.1)
@@ -205,7 +208,6 @@ Our results confirm that continual pre-training of Falcon-7b and Mistral-7B-v0.1
 
 Ranking results also reveal a clear subjective preference for Claire-7B-0.1,
 as shown in the following table:
-<!--|                               | **Claire-Falcon** | **Claire-Mistral** | **Falcon** | **Mistral** | -->
 | | <span style="font-weight: normal">... over</span><br /> **Claire-Falcon** | <span style="font-weight: normal">... over</span><br /> **Claire-Mistral** | <span style="font-weight: normal">... over</span><br /> **Falcon** | <span style="font-weight: normal">... over</span><br /> **Mistral** |
 |--------------------------------------|----------------------|-----------------------|---------------|---------------------|
 | prefer<br /> **Claire-Falcon** ...  |                      | **62.2%**             | **63.9%**     | **83.8%**           |
@@ -220,20 +222,20 @@ as shown in the following table:
 and "Claire-Mistral", for [Claire-Mistral-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-Mistral-7B-0.1).)
 
 Please note that the model can generate disfluencies and humorous responses as a result of its training on spoken and theatrical text. 
-
+-->
 
 ## License
 
 Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
-Claire-7B-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+Claire-7B-EN-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
 
-You can find a variant of this model published under the Apache 2.0 license at [OpenLLM-France/Claire-7B-Apache-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-Apache-0.1).
+<!-- You can find a variant of this model published under the Apache 2.0 license at [OpenLLM-France/Claire-7B-Apache-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-Apache-0.1). -->
 
 ## Acknowledgements
 
 This work was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014561). 
 
-Claire-7B-0.1 was created by members of [LINAGORA](https://labs.linagora.com/) (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.
+Claire-7B-EN-0.1 was created by members of [LINAGORA](https://labs.linagora.com/) (in alphabetical order): Ismaïl Harrando, Julie Hunter, Jean-Pierre Lorré, Jérôme Louradour, Michel-Marie Maudet, Virgile Rennard, Guokan Shang.
 
 Special thanks to partners from the OpenLLM-France community, especially Christophe Cerisara (LORIA), Pierre-Carl Langlais and Anastasia Stasenko (OpSci), and Pierre Colombo, for valuable advice.