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Model: rasa/cmd_gen_codellama_13b_calm_demo Source: Original Platform
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README.md
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---
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base_model:
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- codellama/CodeLlama-13b-Instruct-hf
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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datasets:
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- rasa/command-generation-calm-demo-v1
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pipeline_tag: text-generation
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---
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# Model Card for Command Generator
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<!-- Provide a quick summary of what the model is/does. -->
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This is a Dialogue Understanding (DU) model developed by Rasa.
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It can be used to power assistants built with the [Conversational AI with Language Models (CALM) approach](https://rasa.com/docs/rasa-pro/calm) developed by Rasa.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This model takes as input a transcript of an ongoing conversation between an AI assistant and a user,
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as well as structured information about the assistant's business logic.
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As output, it produces a short sequence of [commands](https://rasa.com/docs/rasa-pro/concepts/dialogue-understanding#command-reference)
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(typically 1-3) from the following list:
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* `StartFlow(flow_name)`
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* `SetSlot(slot_name, slot_value)`
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* `CorrectSlot(slot_name, slot_value)`
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* `Clarify(flow_name_1, flow_name_2, ...)`
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* `ChitChat`
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* `KnowledgeAnswer`
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* `HumanHandoff`
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* `Error`
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Note that this model can only produce commands to be interpreted by Rasa.
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It **cannot** be used to generate arbitrary text.
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The Command Generator translates user messages into this internal grammar, allowing CALM to progress the conversation.
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Examples:
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> I want to transfer money
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`StartFlow(transfer_money)`
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> I want to transfer $55 to John
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`StartFlow(transfer_money), SetSlot(recipient, John), SetSlot(amount, 55)`
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- **Developed by:** Rasa Technologies
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- **Model type:** Text Generation
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model [optional]:** [CodeLlama 13b Instruct](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf)
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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The Command Generator is used as part of an AI assistant developed with Rasa's CALM paradigm.
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Typical use cases include customer-facing chatbots, voice assistants, IVR systems, and internal chatbots in large organizations.
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### Direct Use
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This model can be directly used as part of the command generator component if the flows of your CALM assistant are similar to flows used in the [rasa-calm-demo assistant](https://github.com/RasaHQ/rasa-calm-demo).
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### Downstream Use [optional]
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The model can also be used as a base model to fine-tune further on your own assistant's data using the [fine-tuning recipe feature](https://rasa.com/docs/rasa-pro/building-assistants/fine-tuning-recipe#step-2-prepare-the-fine-tuning-dataset) available in rasa pro
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### Out-of-Scope Use
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Since the model has been explicitly fine-tuned to output the grammar of commands, it shouldn't be used to generate any other free form content.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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The Command Generator interprets conversations and translates user messages into commands.
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These commands are processed by Rasa to advance the conversations.
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This model does not generate text to be sent to and end user, and is incapable of generating problematic
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or harmful text.
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However, as with any pre-trained model, its predictions are susceptible to bias.
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For example, the accuracy of the model varies with the language used. The authors have tested the performance on English but haven't tried the model in any other language.
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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Trained on the `train` split of [rasa/command-generation-calm-demo-v1](https://huggingface.co/datasets/rasa/command-generation-calm-demo-v1).
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### Training Procedure
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Trained using the notebook available [here](https://github.com/RasaHQ/notebooks/blob/main/cmd_gen_finetuning.ipynb). Used a single A100 GPU with 80GB VRAM.
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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Evaluated on the `test` split of [rasa/command-generation-calm-demo-v1](https://huggingface.co/datasets/rasa/command-generation-calm-demo-v1).
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#### Metrics
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F1 score per command type (StartFlow, SetSlot, etc.) is the main metric chosen to evaluate the model on the test split.
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This helps us understand which commands the model has learnt well and the ones that the model needs more training on.
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### Results
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The below table shows the F1 score per command for this model.
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| Model | StartFlow | SetSlot | Cancel | Clarify | Chitchat | SearchAndReply | SkipQuestion |
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|--------------------------------------|-----------|---------|--------|---------|----------|----------------|--------------|
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| rasa/cmd_gen_codellama_13b_calm_demo | 0.9722 | 0.9239 | 0.6667 | 0.8889 | 1 | 0 | 0.8 |
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## Model Card Contact
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[More Information Needed]
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config.json
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"torch_dtype": "bfloat16",
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"transformers_version": "4.42.4",
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"use_cache": true,
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"vocab_size": 32016
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}
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|
||||
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|
||||
}
|
||||
}
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"▁<PRE>",
|
||||
"▁<MID>",
|
||||
"▁<SUF>",
|
||||
"▁<EOT>",
|
||||
"▁<PRE>",
|
||||
"▁<MID>",
|
||||
"▁<SUF>",
|
||||
"▁<EOT>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<unk>",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93455
tokenizer.json
Normal file
93455
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
|
||||
size 500058
|
||||
89
tokenizer_config.json
Normal file
89
tokenizer_config.json
Normal file
@@ -0,0 +1,89 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32007": {
|
||||
"content": "▁<PRE>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32008": {
|
||||
"content": "▁<SUF>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32009": {
|
||||
"content": "▁<MID>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32010": {
|
||||
"content": "▁<EOT>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"▁<PRE>",
|
||||
"▁<MID>",
|
||||
"▁<SUF>",
|
||||
"▁<EOT>",
|
||||
"▁<PRE>",
|
||||
"▁<MID>",
|
||||
"▁<SUF>",
|
||||
"▁<EOT>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + eos_token }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"eot_token": "▁<EOT>",
|
||||
"fill_token": "<FILL_ME>",
|
||||
"legacy": null,
|
||||
"middle_token": "▁<MID>",
|
||||
"model_max_length": 16384,
|
||||
"pad_token": "<unk>",
|
||||
"padding_side": "left",
|
||||
"prefix_token": "▁<PRE>",
|
||||
"sp_model_kwargs": {},
|
||||
"suffix_token": "▁<SUF>",
|
||||
"tokenizer_class": "CodeLlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user