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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 12:27:52
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| library_name
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jondurbin/airoboros-l2-7b-2.2
|
jondurbin
| 2023-09-12T07:42:03Z | 6 | 6 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-2.2",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-10T23:00:27Z |
---
license: llama2
datasets:
- jondurbin/airoboros-2.2
---
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
Highlights:
- The prompt format has changed! It is now newlines instead of spaces between system/USER/ASSISTANT (see prompt info below).
- "Clean" version of airoboros-2.2 dataset -- this model __does not__ contain the de-alignment data.
- For an uncensored version, use spicyboros variants.
- I re-generated all of the outputs in the dataset that had "Once upon a time" so they'd be less cliche - no guarantees that won't still happen, but in theory it may happen less.
- More multiple choice, better awareness, some alignment for normal use case but system-prompt overridable etc.
Breakdown of the training data:
| Count | Category |
|-------|----------------------------|
| 36 | experience |
| 60 | quiz |
| 63 | card |
| 76 | greeting |
| 100 | detailed\_writing |
| 200 | song |
| 204 | editor |
| 207 | counterfactual\_contextual |
| 268 | cot |
| 339 | theory\_of\_mind |
| 416 | awareness |
| 439 | stylized\_response |
| 457 | misconception |
| 500 | summarization |
| 620 | riddle |
| 719 | agent |
| 800 | plan |
| 873 | gtkm |
| 963 | rp |
| 1000 | wordgame |
| 1279 | multiple\_choice |
| 1519 | joke |
| 1758 | writing |
| 2152 | contextual |
| 2183 | trivia |
| 2364 | roleplay |
| 4699 | general |
| 5775 | coding |
| 11366 | orca |
In other words, it's a fairly general purpose model, but focuses fairly heavily on instruction response pairs rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format:
```
A chat.
USER: {prompt}
ASSISTANT:
```
The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc.
Here's another example:
```
A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously.
USER: {prompt}
ASSISTANT:
```
And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf).
```
A chat between old friends: Timmy and Tommy.
{description of characters}
{setting for the chat}
Timmy: *takes a big sip from his coffee* "Ah, sweet, delicious, magical coffee."
Tommy:
```
__*I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.*__
### Fine tuning info
https://gist.github.com/jondurbin/7aabf2d8e1ded7b29897a3dd95d81e01
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.2 models are built on top of llama-2/codellama.
The llama-2 base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
veezbo/LLama-2-7b-hf-akkadian
|
veezbo
| 2023-09-12T07:39:08Z | 6 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-04T22:41:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
osanseviero/test-transf
|
osanseviero
| 2023-09-12T07:37:26Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-12T07:37:11Z |
---
library_name: transformers
pipeline_tag: text-generation
---
|
Fynd/cleaned_v7_complete_llamav2_13b_intent_6_ep
|
Fynd
| 2023-09-12T07:37:07Z | 9 | 0 |
peft
|
[
"peft",
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2023-09-11T09:59:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
mikr/whisper-medium-czech-cv11
|
mikr
| 2023-09-12T07:30:08Z | 15 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"hf-asr-leaderboard",
"generated_from_trainer",
"cs",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-11T16:06:38Z |
---
language:
- cs
license: apache-2.0
tags:
- whisper-event
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium Czech 2 CV11
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: cs
split: test
metrics:
- type: wer
value: 11.408629675328264
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium Czech 2 CV11
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2417
- Wer: 11.4086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0105 | 4.24 | 1000 | 0.1973 | 12.6130 |
| 0.0016 | 8.47 | 2000 | 0.2198 | 11.8985 |
| 0.0004 | 12.71 | 3000 | 0.2310 | 11.4547 |
| 0.0003 | 16.95 | 4000 | 0.2380 | 11.4270 |
| 0.0002 | 21.19 | 5000 | 0.2417 | 11.4086 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
nickprock/distilbert-base-uncased-banking77-classification
|
nickprock
| 2023-09-12T07:26:36Z | 116 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T08:51:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
widget:
- text: 'Can I track the card you sent to me? '
example_title: Card Arrival Example
- text: Can you explain your exchange rate policy to me?
example_title: Exchange Rate Example
- text: I can't pay by my credit card
example_title: Card Not Working Example
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-banking77-classification
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
args: default
metrics:
- type: accuracy
value: 0.924025974025974
name: Accuracy
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
config: default
split: test
metrics:
- type: accuracy
value: 0.924025974025974
name: Accuracy
verified: true
- type: precision
value: 0.9278003086307286
name: Precision Macro
verified: true
- type: precision
value: 0.924025974025974
name: Precision Micro
verified: true
- type: precision
value: 0.9278003086307287
name: Precision Weighted
verified: true
- type: recall
value: 0.9240259740259743
name: Recall Macro
verified: true
- type: recall
value: 0.924025974025974
name: Recall Micro
verified: true
- type: recall
value: 0.924025974025974
name: Recall Weighted
verified: true
- type: f1
value: 0.9243068139192414
name: F1 Macro
verified: true
- type: f1
value: 0.924025974025974
name: F1 Micro
verified: true
- type: f1
value: 0.9243068139192416
name: F1 Weighted
verified: true
- type: loss
value: 0.31516405940055847
name: loss
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-banking77-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3152
- Accuracy: 0.9240
- F1 Score: 0.9243
## Model description
This is my first fine-tuning experiment using Hugging Face.
Using distilBERT as a pretrained model, I trained a classifier for online banking queries.
It could be useful for addressing tickets.
## Intended uses & limitations
The model can be used on text classification. In particular is fine tuned on banking domain.
## Training and evaluation data
The dataset used is [banking77](https://huggingface.co/datasets/banking77)
The 77 labels are:
|label|intent|
|:---:|:----:|
|0|activate_my_card|
|1|age_limit|
|2|apple_pay_or_google_pay|
|3|atm_support|
|4|automatic_top_up|
|5|balance_not_updated_after_bank_transfer|
|6|balance_not_updated_after_cheque_or_cash_deposit|
|7|beneficiary_not_allowed|
|8|cancel_transfer|
|9|card_about_to_expire|
|10|card_acceptance|
|11|card_arrival|
|12|card_delivery_estimate|
|13|card_linking|
|14|card_not_working|
|15|card_payment_fee_charged|
|16|card_payment_not_recognised|
|17|card_payment_wrong_exchange_rate|
|18|card_swallowed|
|19|cash_withdrawal_charge|
|20|cash_withdrawal_not_recognised|
|21|change_pin|
|22|compromised_card|
|23|contactless_not_working|
|24|country_support|
|25|declined_card_payment|
|26|declined_cash_withdrawal|
|27|declined_transfer|
|28|direct_debit_payment_not_recognised|
|29|disposable_card_limits|
|30|edit_personal_details|
|31|exchange_charge|
|32|exchange_rate|
|33|exchange_via_app|
|34|extra_charge_on_statement|
|35|failed_transfer|
|36|fiat_currency_support|
|37|get_disposable_virtual_card|
|38|get_physical_card|
|39|getting_spare_card|
|40|getting_virtual_card|
|41|lost_or_stolen_card|
|42|lost_or_stolen_phone|
|43|order_physical_card|
|44|passcode_forgotten|
|45|pending_card_payment|
|46|pending_cash_withdrawal|
|47|pending_top_up|
|48|pending_transfer|
|49|pin_blocked|
|50|receiving_money|
|51|Refund_not_showing_up|
|52|request_refund|
|53|reverted_card_payment?|
|54|supported_cards_and_currencies|
|55|terminate_account|
|56|top_up_by_bank_transfer_charge|
|57|top_up_by_card_charge|
|58|top_up_by_cash_or_cheque|
|59|top_up_failed|
|60|top_up_limits|
|61|top_up_reverted|
|62|topping_up_by_card|
|63|transaction_charged_twice|
|64|transfer_fee_charged|
|65|transfer_into_account|
|66|transfer_not_received_by_recipient|
|67|transfer_timing|
|68|unable_to_verify_identity|
|69|verify_my_identity|
|70|verify_source_of_funds|
|71|verify_top_up|
|72|virtual_card_not_working|
|73|visa_or_mastercard|
|74|why_verify_identity|
|75|wrong_amount_of_cash_received|
|76|wrong_exchange_rate_for_cash_withdrawal|
## Training procedure
```
from transformers import pipeline
pipe = pipeline("text-classification", model="nickprock/distilbert-base-uncased-banking77-classification")
pipe("I can't pay by my credit card")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 3.8732 | 1.0 | 157 | 3.1476 | 0.5370 | 0.4881 |
| 2.5598 | 2.0 | 314 | 1.9780 | 0.6916 | 0.6585 |
| 1.5863 | 3.0 | 471 | 1.2239 | 0.8042 | 0.7864 |
| 0.9829 | 4.0 | 628 | 0.8067 | 0.8565 | 0.8487 |
| 0.6274 | 5.0 | 785 | 0.5837 | 0.8799 | 0.8752 |
| 0.4304 | 6.0 | 942 | 0.4630 | 0.9042 | 0.9040 |
| 0.3106 | 7.0 | 1099 | 0.3982 | 0.9088 | 0.9087 |
| 0.2238 | 8.0 | 1256 | 0.3587 | 0.9110 | 0.9113 |
| 0.1708 | 9.0 | 1413 | 0.3351 | 0.9208 | 0.9208 |
| 0.1256 | 10.0 | 1570 | 0.3242 | 0.9179 | 0.9182 |
| 0.0981 | 11.0 | 1727 | 0.3136 | 0.9211 | 0.9214 |
| 0.0745 | 12.0 | 1884 | 0.3151 | 0.9211 | 0.9213 |
| 0.0601 | 13.0 | 2041 | 0.3089 | 0.9218 | 0.9220 |
| 0.0482 | 14.0 | 2198 | 0.3158 | 0.9214 | 0.9216 |
| 0.0402 | 15.0 | 2355 | 0.3126 | 0.9224 | 0.9226 |
| 0.0344 | 16.0 | 2512 | 0.3143 | 0.9231 | 0.9233 |
| 0.0298 | 17.0 | 2669 | 0.3156 | 0.9231 | 0.9233 |
| 0.0272 | 18.0 | 2826 | 0.3134 | 0.9244 | 0.9247 |
| 0.0237 | 19.0 | 2983 | 0.3156 | 0.9244 | 0.9246 |
| 0.0229 | 20.0 | 3140 | 0.3152 | 0.9240 | 0.9243 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nickprock/distilbert-finetuned-ner-ontonotes
|
nickprock
| 2023-09-12T07:26:29Z | 131 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:tner/ontonotes5",
"base_model:distilbert/distilbert-base-cased",
"base_model:finetune:distilbert/distilbert-base-cased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-26T10:27:27Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- tner/ontonotes5
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: 'I am Jack. I live in California and I work at Apple '
example_title: Example 1
- text: 'Wow this book is amazing and costs only 4€ '
example_title: Example 2
base_model: distilbert-base-cased
model-index:
- name: distilbert-finetuned-ner-ontonotes
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: ontonotes5
type: ontonotes5
config: ontonotes5
split: train
args: ontonotes5
metrics:
- type: precision
value: 0.8535359959297889
name: Precision
- type: recall
value: 0.8788553467356427
name: Recall
- type: f1
value: 0.8660106468785288
name: F1
- type: accuracy
value: 0.9749625470373822
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-finetuned-ner-ontonotes
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1448
- Precision: 0.8535
- Recall: 0.8789
- F1: 0.8660
- Accuracy: 0.9750
## Model description
Token classification experiment, NER, on business topics.
## Intended uses & limitations
The model can be used on token classification, in particular NER. It is fine tuned on business domain.
## Training and evaluation data
The dataset used is [ontonotes5](https://huggingface.co/datasets/tner/ontonotes5)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0937 | 1.0 | 7491 | 0.0998 | 0.8367 | 0.8587 | 0.8475 | 0.9731 |
| 0.0572 | 2.0 | 14982 | 0.1084 | 0.8338 | 0.8759 | 0.8543 | 0.9737 |
| 0.0403 | 3.0 | 22473 | 0.1145 | 0.8521 | 0.8707 | 0.8613 | 0.9748 |
| 0.0265 | 4.0 | 29964 | 0.1222 | 0.8535 | 0.8815 | 0.8672 | 0.9752 |
| 0.0148 | 5.0 | 37455 | 0.1365 | 0.8536 | 0.8770 | 0.8651 | 0.9747 |
| 0.0111 | 6.0 | 44946 | 0.1448 | 0.8535 | 0.8789 | 0.8660 | 0.9750 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_en3_0015
|
bigmorning
| 2023-09-12T07:18:33Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-12T07:18:24Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0_lr_en3_0015
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0_lr_en3_0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0438
- Train Accuracy: 0.0154
- Train Wermet: 1.1658
- Train Wermet Syl: 1.5877
- Validation Loss: 2.8403
- Validation Accuracy: 0.0133
- Validation Wermet: 0.8122
- Validation Wermet Syl: 0.8351
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 5.0580 | 0.0110 | 1.8635 | 1.7118 | 4.0077 | 0.0112 | 1.0399 | 1.0195 | 0 |
| 4.7330 | 0.0115 | 0.9446 | 0.9262 | 3.9889 | 0.0113 | 0.9757 | 0.9619 | 1 |
| 4.6832 | 0.0116 | 0.8889 | 0.8637 | 3.9832 | 0.0114 | 0.9574 | 0.9276 | 2 |
| 4.6668 | 0.0116 | 0.9362 | 0.9592 | 3.9389 | 0.0113 | 0.9939 | 1.0310 | 3 |
| 4.6252 | 0.0117 | 1.0167 | 1.1679 | 3.9158 | 0.0114 | 0.9633 | 0.9764 | 4 |
| 4.6026 | 0.0117 | 1.0596 | 1.2533 | 3.8896 | 0.0114 | 0.9953 | 1.0541 | 5 |
| 4.5658 | 0.0118 | 1.1222 | 1.3998 | 3.8228 | 0.0116 | 0.9368 | 0.9177 | 6 |
| 4.4715 | 0.0120 | 1.0840 | 1.3327 | 3.7310 | 0.0117 | 0.9284 | 0.9338 | 7 |
| 4.3515 | 0.0122 | 1.5346 | 2.0632 | 3.6182 | 0.0119 | 0.9122 | 0.9112 | 8 |
| 4.1520 | 0.0126 | 1.7418 | 2.3436 | 3.2869 | 0.0124 | 1.3462 | 1.8561 | 9 |
| 3.8516 | 0.0132 | 1.8839 | 2.6071 | 3.0980 | 0.0128 | 0.9534 | 0.9652 | 10 |
| 3.5680 | 0.0139 | 2.4447 | 3.4321 | 2.9746 | 0.0129 | 1.1678 | 1.6347 | 11 |
| 3.3589 | 0.0144 | 2.4698 | 3.4460 | 2.6950 | 0.0137 | 1.0842 | 1.5304 | 12 |
| 3.1696 | 0.0149 | 1.5290 | 2.1570 | 2.5462 | 0.0141 | 0.7527 | 0.8018 | 13 |
| 3.0438 | 0.0154 | 1.1658 | 1.5877 | 2.8403 | 0.0133 | 0.8122 | 0.8351 | 14 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
Vishal24/trained-llama2
|
Vishal24
| 2023-09-12T07:11:58Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T06:50:19Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
WIS/drl-ppo-LunarLander-v2
|
WIS
| 2023-09-12T07:05:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T07:04:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.80 +/- 18.65
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Serotina/a2c-PandaReachDense-v3
|
Serotina
| 2023-09-12T07:04:08Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T06:58:19Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.15 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
santiagotoso/ppo-Huggy
|
santiagotoso
| 2023-09-12T07:02:57Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-12T07:02:52Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: santiagotoso/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
haozhangphy/Taxi-v3
|
haozhangphy
| 2023-09-12T06:57:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T06:57:45Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
model = load_from_hub(repo_id="haozhangphy/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
haozhangphy/q-FrozenLake-v1-4x4-noSlippery
|
haozhangphy
| 2023-09-12T06:54:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T06:54:16Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="haozhangphy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
shaowenchen/baichuan2-7b-chat-gguf
|
shaowenchen
| 2023-09-12T06:42:19Z | 106 | 15 | null |
[
"gguf",
"baichuan",
"chinese",
"text-generation",
"zh",
"en",
"license:other",
"region:us"
] |
text-generation
| 2023-09-11T10:13:53Z |
---
inference: false
language:
- zh
- en
license: other
model_creator: baichuan-inc
model_link: https://www.modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat
model_name: Baichuan2-7B-Chat
model_type: baichuan
pipeline_tag: text-generation
quantized_by: shaowenchen
tasks:
- text2text-generation
tags:
- gguf
- baichuan
- chinese
---
## Provided files
| Name | Quant method | Size |
| ----------------------------- | ------------ | ------ |
| baichuan2-7b-chat.Q2_K.gguf | Q2_K | 3.0 GB |
| baichuan2-7b-chat.Q3_K.gguf | Q3_K | 3.5 GB |
| baichuan2-7b-chat.Q3_K_L.gguf | Q3_K_L | 3.8 GB |
| baichuan2-7b-chat.Q3_K_S.gguf | Q3_K_S | 3.2 GB |
| baichuan2-7b-chat.Q4_0.gguf | Q4_0 | 4.1 GB |
| baichuan2-7b-chat.Q4_1.gguf | Q4_1 | 4.5 GB |
| baichuan2-7b-chat.Q4_K.gguf | Q4_K | 4.3 GB |
| baichuan2-7b-chat.Q4_K_S.gguf | Q4_K_S | 4.1 GB |
| baichuan2-7b-chat.Q5_0.gguf | Q5_0 | 4.9 GB |
| baichuan2-7b-chat.Q5_1.gguf | Q5_1 | 5.3 GB |
| baichuan2-7b-chat.Q5_K.gguf | Q5_K | 5.0 GB |
| baichuan2-7b-chat.Q5_K_S.gguf | Q5_K_S | 4.9 GB |
| baichuan2-7b-chat.Q6_K.gguf | Q6_K | 5.7 GB |
| baichuan2-7b-chat.Q8_0.gguf | Q8_0 | 7.4 GB |
| baichuan2-7b-chat.gguf | full | 14 GB |
Usage:
```
docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest
```
and you can view http://localhost:8000/docs to see the swagger UI.
## Provided images
| Name | Quant method | Size |
| ------------------------------------------- | ------------ | ------- |
| `shaowenchen/baichuan2-7b-chat-gguf:Q2_K` | Q2_K | 7.59 GB |
| `shaowenchen/baichuan2-7b-chat-gguf:Q3_K` | Q3_K | 8.61 GB |
| `shaowenchen/baichuan2-7b-chat-gguf:Q3_K_L` | Q3_K_L | 9.23 GB |
| `shaowenchen/baichuan2-7b-chat-gguf:Q3_K_S` | Q3_K_S | 7.93 GB |
| `shaowenchen/baichuan2-7b-chat-gguf:Q4_0` | Q4_0 | 9.6 GB |
Usage:
```
docker run --rm -p 8000:8000 shaowenchen/baichuan2-7b-chat-gguf:Q2_K
```
and you can view http://localhost:8000/docs to see the swagger UI.
|
jtlicardo/bpmn-information-extraction
|
jtlicardo
| 2023-09-12T06:42:18Z | 141 | 8 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-12-25T21:27:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: The process starts when the customer enters the shop. The customer then takes
the product from the shelf. The customer then pays for the product and leaves
the store.
example_title: Example 1
- text: The process begins when the HR department hires the new employee. Next, the
new employee completes necessary paperwork and provides documentation to the HR
department. After the initial task, the HR department performs a decision to
determine the employee's role and department assignment. The employee is trained
by the Sales department. After the training, the Sales department assigns the
employee a sales quota and performance goals. Finally, the process ends with an
'End' event, when the employee begins their role in the Sales department.
example_title: Example 2
- text: A customer places an order for a product on the company's website. Next, the
customer service department checks the availability of the product and confirms
the order with the customer. After the initial task, the warehouse processes
the order. If the order is eligible for same-day shipping, the warehouse staff
picks and packs the order, and it is sent to the shipping department. After the
order is packed, the shipping department delivers the order to the customer. Finally,
the process ends with an 'End' event, when the customer receives their order.
example_title: Example 3
base_model: bert-base-cased
model-index:
- name: bert-finetuned-v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bpmn-information-extraction
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a dataset containing 90 textual process descriptions.
The dataset contains 5 target labels:
* `AGENT`
* `TASK`
* `TASK_INFO`
* `PROCESS_INFO`
* `CONDITION`
It achieves the following results on the evaluation set:
- Loss: 0.2909
- Precision: 0.8557
- Recall: 0.9247
- F1: 0.8889
- Accuracy: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.0586 | 1.0 | 10 | 1.5601 | 0.1278 | 0.1559 | 0.1404 | 0.4750 |
| 1.3702 | 2.0 | 20 | 1.0113 | 0.3947 | 0.5645 | 0.4646 | 0.7150 |
| 0.8872 | 3.0 | 30 | 0.6645 | 0.5224 | 0.6882 | 0.5940 | 0.8051 |
| 0.5341 | 4.0 | 40 | 0.4741 | 0.6754 | 0.8280 | 0.7440 | 0.8541 |
| 0.3221 | 5.0 | 50 | 0.3831 | 0.7523 | 0.8817 | 0.8119 | 0.8883 |
| 0.2168 | 6.0 | 60 | 0.3297 | 0.7731 | 0.8978 | 0.8308 | 0.9079 |
| 0.1565 | 7.0 | 70 | 0.2998 | 0.8195 | 0.9032 | 0.8593 | 0.9128 |
| 0.1227 | 8.0 | 80 | 0.3227 | 0.8038 | 0.9032 | 0.8506 | 0.9099 |
| 0.0957 | 9.0 | 90 | 0.2840 | 0.8431 | 0.9247 | 0.8821 | 0.9216 |
| 0.077 | 10.0 | 100 | 0.2914 | 0.8252 | 0.9140 | 0.8673 | 0.9216 |
| 0.0691 | 11.0 | 110 | 0.2850 | 0.8431 | 0.9247 | 0.8821 | 0.9285 |
| 0.059 | 12.0 | 120 | 0.2886 | 0.8564 | 0.9301 | 0.8918 | 0.9285 |
| 0.0528 | 13.0 | 130 | 0.2838 | 0.8564 | 0.9301 | 0.8918 | 0.9305 |
| 0.0488 | 14.0 | 140 | 0.2881 | 0.8515 | 0.9247 | 0.8866 | 0.9305 |
| 0.049 | 15.0 | 150 | 0.2909 | 0.8557 | 0.9247 | 0.8889 | 0.9285 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
nichelia/Llama-2-7B-bf16-sharded-quotes
|
nichelia
| 2023-09-12T06:32:57Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T06:32:55Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
saattrupdan/job-listing-relevance-model
|
saattrupdan
| 2023-09-12T06:20:28Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-22T16:56:45Z |
---
license: mit
tags:
- generated_from_trainer
base_model: xlm-roberta-base
model-index:
- name: job-listing-relevance-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# job-listing-relevance-model
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7435 | 0.43 | 50 | 0.6889 |
| 0.3222 | 0.87 | 100 | 0.2906 |
| 0.2573 | 1.3 | 150 | 0.1937 |
| 0.1205 | 1.74 | 200 | 0.1411 |
| 0.1586 | 2.17 | 250 | 0.2008 |
| 0.0755 | 2.61 | 300 | 0.1926 |
| 0.062 | 3.04 | 350 | 0.2257 |
| 0.0644 | 3.48 | 400 | 0.1497 |
| 0.1034 | 3.91 | 450 | 0.1561 |
| 0.008 | 4.35 | 500 | 0.2067 |
| 0.0616 | 4.78 | 550 | 0.2067 |
| 0.0766 | 5.22 | 600 | 0.1494 |
| 0.0029 | 5.65 | 650 | 0.2078 |
| 0.1076 | 6.09 | 700 | 0.1669 |
| 0.0025 | 6.52 | 750 | 0.1564 |
| 0.0498 | 6.95 | 800 | 0.2355 |
| 0.0011 | 7.39 | 850 | 0.1652 |
| 0.0271 | 7.82 | 900 | 0.1731 |
| 0.012 | 8.26 | 950 | 0.1590 |
| 0.0257 | 8.69 | 1000 | 0.1638 |
| 0.0009 | 9.13 | 1050 | 0.1851 |
| 0.0013 | 9.56 | 1100 | 0.1613 |
| 0.0015 | 10.0 | 1150 | 0.1649 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Snehalb/Email_classification_1209
|
Snehalb
| 2023-09-12T06:15:14Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T06:01:26Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Snehalb/Email_classification_1209
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Snehalb/Email_classification_1209
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6061
- Validation Loss: 1.5591
- Train Accuracy: 0.4462
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 20, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.6061 | 1.5591 | 0.4462 | 0 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
imamassi/Visual_Emotional_Analysis
|
imamassi
| 2023-09-12T06:15:14Z | 191 | 1 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-12T06:00:23Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Visual_Emotional_Analysis
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.44375
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Visual_Emotional_Analysis
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6616
- Accuracy: 0.4437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7478 | 1.0 | 10 | 1.8034 | 0.4437 |
| 1.6809 | 2.0 | 20 | 1.6970 | 0.4437 |
| 1.616 | 3.0 | 30 | 1.6712 | 0.4625 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
rideadragon/dqn-SpaceInvadersNoFrameskip-v4
|
rideadragon
| 2023-09-12T06:12:46Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T06:12:10Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 591.00 +/- 265.13
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rideadragon -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rideadragon -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rideadragon
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
ahsan-mavros/balanced-test
|
ahsan-mavros
| 2023-09-12T06:07:21Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-12T06:06:51Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: balanced-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# balanced-test
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1274
- Rouge1: 94.4211
- Rouge2: 78.4814
- Rougel: 94.42
- Rougelsum: 94.4244
- Gen Len: 5.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:|
| 0.1305 | 1.0 | 2500 | 0.1274 | 94.4211 | 78.4814 | 94.42 | 94.4244 | 5.0 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ckeisc/lora_test
|
ckeisc
| 2023-09-12T05:59:40Z | 5 | 0 |
diffusers
|
[
"diffusers",
"region:us"
] | null | 2023-09-12T05:51:21Z |
---
library_name: diffusers
---
|
sophie04/sd-class-butterflies-32
|
sophie04
| 2023-09-12T05:52:22Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-09-12T05:52:13Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('sophie04/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
sazzad-sit/whisper-small-bn-3ds
|
sazzad-sit
| 2023-09-12T05:48:07Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-17T05:19:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: whisper-small-bn-3ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-bn-3ds
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0574
- Wer: 8.5038
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8950
- training_steps: 28000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2337 | 0.22 | 2000 | 0.2298 | 31.7734 |
| 0.1444 | 0.45 | 4000 | 0.1516 | 22.2382 |
| 0.1106 | 0.67 | 6000 | 0.1181 | 17.2230 |
| 0.0933 | 0.89 | 8000 | 0.1005 | 14.8008 |
| 0.0747 | 1.12 | 10000 | 0.0865 | 12.7184 |
| 0.0642 | 1.34 | 12000 | 0.0788 | 11.6441 |
| 0.0613 | 1.56 | 14000 | 0.0715 | 10.6524 |
| 0.0565 | 1.79 | 16000 | 0.0666 | 9.8807 |
| 0.0494 | 2.01 | 18000 | 0.0640 | 9.5265 |
| 0.037 | 2.24 | 20000 | 0.0619 | 9.0317 |
| 0.0348 | 2.46 | 22000 | 0.0600 | 9.0329 |
| 0.0329 | 2.68 | 24000 | 0.0582 | 8.8407 |
| 0.0321 | 2.91 | 26000 | 0.0564 | 8.4931 |
| 0.0227 | 3.13 | 28000 | 0.0574 | 8.5038 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.2.dev0
- Tokenizers 0.13.2
|
RyyyT/dqn-SpaceInvadersNoFrameskip-v4
|
RyyyT
| 2023-09-12T05:42:44Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T05:42:12Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 517.00 +/- 126.91
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RyyyT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RyyyT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga RyyyT
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
kyuwon416/Reinforce-Pixelcopter-PLE-v0
|
kyuwon416
| 2023-09-12T05:30:54Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T05:30:50Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.50 +/- 23.63
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
r4ghu/distilbert-base-uncased-finetuned-emotion
|
r4ghu
| 2023-09-12T05:23:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T05:14:51Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9225
- name: F1
type: f1
value: 0.9223771867794991
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2258
- Accuracy: 0.9225
- F1: 0.9224
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8691 | 1.0 | 250 | 0.3363 | 0.904 | 0.9025 |
| 0.2702 | 2.0 | 500 | 0.2258 | 0.9225 | 0.9224 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
BubbleJoe/Reinforce-CartPole8
|
BubbleJoe
| 2023-09-12T05:13:11Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T05:13:01Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
almas-alz/vgg_unet_barley_weed_segmentation
|
almas-alz
| 2023-09-12T05:08:07Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-09-12T05:07:54Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
Geotrend/bert-base-en-nl-cased
|
Geotrend
| 2023-09-12T05:07:32Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# bert-base-en-nl-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-nl-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-en-nl-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
aatherton2024/marian-finetuned-kde4-en-to-fr
|
aatherton2024
| 2023-09-12T05:06:57Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-09-11T15:19:41Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.8073278441039
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8561
- Bleu: 52.8073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.2
|
newronai/clma2-13b-Chat-Adapter-Unvalidated-MutuallyExclusive
|
newronai
| 2023-09-12T05:03:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T05:03:34Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
badhorse666/Reinforce-pixelcopter
|
badhorse666
| 2023-09-12T04:58:52Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T04:58:48Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.00 +/- 27.06
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
substratusai/Llama-2-7b-chat-GGUF
|
substratusai
| 2023-09-12T04:58:13Z | 37 | 1 | null |
[
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"en",
"arxiv:2307.09288",
"region:us"
] |
text-generation
| 2023-09-11T15:58:51Z |
---
language:
- en
pipeline_tag: text-generation
inference: false
arxiv: 2307.09288
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# Llama 2 7B Chat GGUF version
Files provided:
| File | Description |
| -------- | ------- |
| llama2-7b-chat-Q4_K_M.gguf | Quantised GGUF model using Q4_K_M |
| llama2-7b-chat-Q5_K_S.gguf | Quantised GGUF model using Q5_K_S |
| llama2-7b-chat-Q6_K.gguf | Quantised GGUF model using Q6_K |
| llama2-7b-chat-f16.gguf | Original float16 format which can be used for further quantisation |
Need help? Join the [Substratus discord server](https://discord.gg/JeXhcmjZVm).
Original README:
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
MorganDusty/RazDva
|
MorganDusty
| 2023-09-12T04:54:38Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-09-12T04:54:38Z |
---
license: cc-by-nc-sa-4.0
---
|
Zetatech/pvt-tiny-224
|
Zetatech
| 2023-09-12T04:51:39Z | 1,713 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"pvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2102.12122",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-24T00:53:31Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Pyramid Vision Transformer (tiny-sized model)
Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in [this repository](https://github.com/whai362/PVT).
Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya).
## Model description
The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of variable-size patches, which are linearly embedded. Unlike ViT models, PVT is using a progressive shrinking pyramid to reduce computations of large feature maps at each stage. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/Xrenya) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import PvtImageProcessor, PvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = PvtImageProcessor.from_pretrained('Zetatech/pvt-tiny-224')
model = PvtForImageClassification.from_pretrained('Zetatech/pvt-tiny-224')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/pvt.html#).
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/whai362/PVT/blob/v2/classification/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2021pyramid,
title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={568--578},
year={2021}
}
```
|
preetham/illus2
|
preetham
| 2023-09-12T04:19:52Z | 32 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-12T04:04:33Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks illustration
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - preetham/illus2
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks illustration using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
xbb123456/distilbert-base-uncased-finetuned-cola
|
xbb123456
| 2023-09-12T04:14:18Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T02:42:27Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5030064208528437
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6024
- Matthews Correlation: 0.5030
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 268 | 0.4818 | 0.4647 |
| 0.4548 | 2.0 | 536 | 0.4927 | 0.4904 |
| 0.4548 | 3.0 | 804 | 0.5750 | 0.4770 |
| 0.2322 | 4.0 | 1072 | 0.6024 | 0.5030 |
| 0.2322 | 5.0 | 1340 | 0.6697 | 0.5029 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
RaulWCosta/t5-large_PREFIX_TUNING_SEQ2SEQ_5f2446
|
RaulWCosta
| 2023-09-12T03:51:45Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T03:51:42Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
lancewilhelm/lora-trained-xl-colab
|
lancewilhelm
| 2023-09-12T03:40:18Z | 1 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-09-11T03:09:18Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - lancewilhelm/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
buddhilive/bert-finetuned-squad
|
buddhilive
| 2023-09-12T03:40:14Z | 61 | 1 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-08T03:39:44Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: buddhilive/bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# buddhilive/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0965
- Validation Loss: 0.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0994 | 0.0 | 0 |
| 1.0981 | 0.0 | 1 |
| 1.0965 | 0.0 | 2 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
chinhon/bart-large-cnn_summarizer_30216
|
chinhon
| 2023-09-12T03:39:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-20T15:12:58Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
base_model: facebook/bart-large-cnn
model-index:
- name: bart-large-cnn_summarizer_30216
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn_summarizer_30216
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9800
- Rouge1: 51.6258
- Rouge2: 33.4629
- Rougel: 40.3034
- Rougelsum: 47.8482
- Gen Len: 105.0622
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.8653 | 1.0 | 12086 | 0.9274 | 51.1144 | 32.972 | 39.8981 | 47.2905 | 100.2417 |
| 0.6741 | 2.0 | 24172 | 0.9330 | 51.5965 | 33.5021 | 40.4048 | 47.8046 | 103.9732 |
| 0.4802 | 3.0 | 36258 | 0.9800 | 51.6258 | 33.4629 | 40.3034 | 47.8482 | 105.0622 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
chinhon/pegasus-newsroom-headline_writer
|
chinhon
| 2023-09-12T03:39:10Z | 11 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"base_model:finetune:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
base_model: google/pegasus-newsroom
model-index:
- name: pegasus-newsroom-headline_writer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-headline_writer
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3988
- Rouge1: 41.8748
- Rouge2: 23.1947
- Rougel: 35.6263
- Rougelsum: 35.7355
- Gen Len: 34.1266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5784 | 1.0 | 31200 | 1.4287 | 41.4257 | 22.9355 | 35.3299 | 35.4648 | 34.4677 |
| 1.3501 | 2.0 | 62400 | 1.3955 | 41.9119 | 23.1912 | 35.6698 | 35.7479 | 33.8672 |
| 1.2417 | 3.0 | 93600 | 1.3988 | 41.8748 | 23.1947 | 35.6263 | 35.7355 | 34.1266 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
chinhon/pegasus-multi_news-headline
|
chinhon
| 2023-09-12T03:38:52Z | 375 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"base_model:finetune:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
base_model: google/pegasus-multi_news
model-index:
- name: pegasus-multi_news-headline
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-headline
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4421
- Rouge1: 41.616
- Rouge2: 22.922
- Rougel: 35.2189
- Rougelsum: 35.3561
- Gen Len: 33.9532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6637 | 1.0 | 31200 | 1.4877 | 41.0996 | 22.579 | 34.9311 | 35.0611 | 34.3431 |
| 1.4395 | 2.0 | 62400 | 1.4388 | 41.6075 | 22.8274 | 35.2051 | 35.3526 | 33.7965 |
| 1.3137 | 3.0 | 93600 | 1.4421 | 41.616 | 22.922 | 35.2189 | 35.3561 | 33.9532 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
bongo2112/sdxl-db-moodewji
|
bongo2112
| 2023-09-12T03:28:16Z | 3 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-11T21:31:42Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of moodewjitz person
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
folflo/mt5-small-finetuned-HunSum-1_v0911
|
folflo
| 2023-09-12T03:20:49Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-11T19:36:53Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_keras_callback
model-index:
- name: folflo/mt5-small-finetuned-HunSum-1_v0911
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# folflo/mt5-small-finetuned-HunSum-1_v0911
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0937
- Validation Loss: 2.8512
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 45488, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.8838 | 3.2449 | 0 |
| 3.6730 | 3.0674 | 1 |
| 3.3378 | 2.9587 | 2 |
| 3.1802 | 2.9047 | 3 |
| 3.0937 | 2.8512 | 4 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
grkrish/my_awesome_qa_model
|
grkrish
| 2023-09-12T03:19:44Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-10T15:17:12Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: grkrish/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# grkrish/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.9801
- Validation Loss: 4.3666
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.6719 | 4.4302 | 0 |
| 4.1300 | 4.3666 | 1 |
| 3.9801 | 4.3666 | 2 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
hw2942/chinese-pert-base-SSEC
|
hw2942
| 2023-09-12T03:17:25Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:hfl/chinese-pert-base",
"base_model:finetune:hfl/chinese-pert-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T03:11:42Z |
---
license: cc-by-nc-sa-4.0
base_model: hfl/chinese-pert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSEC-v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-pert-base-wallstreetcn-morning-news-market-overview-SSEC-v4
This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4333
- Accuracy: 0.7188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 34 | 0.9037 | 0.75 |
| No log | 2.0 | 68 | 1.1279 | 0.7188 |
| No log | 3.0 | 102 | 1.1238 | 0.75 |
| No log | 4.0 | 136 | 1.4450 | 0.6875 |
| No log | 5.0 | 170 | 1.3506 | 0.7188 |
| No log | 6.0 | 204 | 1.4783 | 0.6875 |
| No log | 7.0 | 238 | 1.2780 | 0.75 |
| No log | 8.0 | 272 | 1.4154 | 0.7188 |
| No log | 9.0 | 306 | 1.4328 | 0.7188 |
| No log | 10.0 | 340 | 1.4333 | 0.7188 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Luciano/xlm-roberta-large-finetuned-lener-br
|
Luciano
| 2023-09-12T03:16:53Z | 53 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"pt",
"dataset:lener_br",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-15T14:26:45Z |
---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
base_model: xlm-roberta-large
model-index:
- name: xlm-roberta-large-finetuned-lener-br
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: train
args: lener_br
metrics:
- type: precision
value: 0.8762313715584744
name: Precision
- type: recall
value: 0.8966141121736882
name: Recall
- type: f1
value: 0.8863055697496168
name: F1
- type: accuracy
value: 0.979500052295785
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-lener-br
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8762
- Recall: 0.8966
- F1: 0.8863
- Accuracy: 0.9795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0785 | 1.0 | 3914 | nan | 0.7119 | 0.8410 | 0.7711 | 0.9658 |
| 0.076 | 2.0 | 7828 | nan | 0.8397 | 0.8679 | 0.8536 | 0.9740 |
| 0.0434 | 3.0 | 11742 | nan | 0.8545 | 0.8666 | 0.8605 | 0.9693 |
| 0.022 | 4.0 | 15656 | nan | 0.8293 | 0.8573 | 0.8431 | 0.9652 |
| 0.0284 | 5.0 | 19570 | nan | 0.8789 | 0.8571 | 0.8678 | 0.9776 |
| 0.029 | 6.0 | 23484 | nan | 0.8521 | 0.8788 | 0.8653 | 0.9771 |
| 0.0227 | 7.0 | 27398 | nan | 0.7648 | 0.8873 | 0.8215 | 0.9686 |
| 0.0219 | 8.0 | 31312 | nan | 0.8609 | 0.9026 | 0.8813 | 0.9780 |
| 0.0121 | 9.0 | 35226 | nan | 0.8746 | 0.8979 | 0.8861 | 0.9812 |
| 0.0087 | 10.0 | 39140 | nan | 0.8829 | 0.8827 | 0.8828 | 0.9808 |
| 0.0081 | 11.0 | 43054 | nan | 0.8740 | 0.8749 | 0.8745 | 0.9765 |
| 0.0058 | 12.0 | 46968 | nan | 0.8838 | 0.8842 | 0.8840 | 0.9788 |
| 0.0044 | 13.0 | 50882 | nan | 0.869 | 0.8984 | 0.8835 | 0.9788 |
| 0.002 | 14.0 | 54796 | nan | 0.8762 | 0.8966 | 0.8863 | 0.9795 |
| 0.0017 | 15.0 | 58710 | nan | 0.8729 | 0.8982 | 0.8854 | 0.9791 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Luciano/xlm-roberta-base-finetuned-lener_br-finetuned-lener-br
|
Luciano
| 2023-09-12T03:15:29Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"pt",
"dataset:lener_br",
"base_model:Luciano/xlm-roberta-base-finetuned-lener_br",
"base_model:finetune:Luciano/xlm-roberta-base-finetuned-lener_br",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-21T16:25:35Z |
---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
base_model: Luciano/xlm-roberta-base-finetuned-lener_br
model-index:
- name: xlm-roberta-base-finetuned-lener_br-finetuned-lener-br
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: train
args: lener_br
metrics:
- type: precision
value: 0.9206349206349206
name: Precision
- type: recall
value: 0.9294391315585423
name: Recall
- type: f1
value: 0.925016077170418
name: F1
- type: accuracy
value: 0.9832504071600401
name: Accuracy
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: validation
metrics:
- type: accuracy
value: 0.9832802904657313
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTk3YTgzYjU4MTQ4ZDU5ZDY1YjNlZTdjNzM1YTY1OGM0ZTcyNTc2NDA4MzFhYmY0NmQ2MDRiMWU3NTUwM2FlZSIsInZlcnNpb24iOjF9.yCQ8lJoSfokChcGn16603Md8wsFG83E_x8ijn1Fuy3dyFmtaHP8UXSzY1pGrWKUnTKeCcOp7W2MD51gP_WRQCA
- type: precision
value: 0.986258771429967
name: Precision
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTYyOTRhOTg2NWY0YTc4NjE1YjU2NGU3NmFlMmQyY2E5N2U2ZmU1YmMzYjZkYmEwYjY1YjcxYWQ3ZTVjMmZlYyIsInZlcnNpb24iOjF9.vP_avJP-puSp3lvxI2lbCsPXfH1lKGCLfrT4hshA_LVn8wjOUPrjgHH60NVM0fjXA35PB0aFnE9qCEvwyfzPBw
- type: recall
value: 0.9897717432152019
name: Recall
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODY1OWU0YzM5ZWE5YmYwOTQ5MGU1MTkzZjkxNDhkOTExZDBlNTI1ODUwMzFlOGUxYTQzZjMxMWNkODZhYWNlNCIsInZlcnNpb24iOjF9.QM6enyQUtL91odii7Iqa1Ya6Yc3S1hM-YYkPLqhqRn7chXPXhB58D7-3dLq_se2rRm7led_kwKBaVZhv7aJBDw
- type: f1
value: 0.9880121346555324
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGEyOWQ5NTViNjZiNGFhNmQ3N2Y4ZWIxYmEwYTM4NjZhMGZkYjc3NWNiN2I5M2YwMjcyYzA3OTlmMWU5MDU1NiIsInZlcnNpb24iOjF9.5VArYd9p24-Wkhnn28wQzpBgKlXhF-fvIFJl6sZasr8FzLAp_yAE9kU8wPGhUc0UW9nsu7PBpH14xbhblsmuBg
- type: loss
value: 0.1050868034362793
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWM0MTM2ODlkMjkxMTUyZDg4YmEyNTEzZjIyZWVkMmJhMGJjMmU1N2JmZDQ3Y2M2ZDZiNmYwZTI2ZjY2MDhmYSIsInZlcnNpb24iOjF9.JRkZwkuXovMIjiGlo38D3TPHImTTizTPf7iquVvoy4uWrdAwNympaMkqU78g9Fpky81-XWhCxK1pmrDhKQPTBg
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-lener_br-finetuned-lener-br
This model is a fine-tuned version of [Luciano/xlm-roberta-base-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-base-finetuned-lener_br) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9206
- Recall: 0.9294
- F1: 0.9250
- Accuracy: 0.9833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0657 | 1.0 | 1957 | nan | 0.7780 | 0.8687 | 0.8209 | 0.9718 |
| 0.0321 | 2.0 | 3914 | nan | 0.8755 | 0.8708 | 0.8731 | 0.9793 |
| 0.0274 | 3.0 | 5871 | nan | 0.8096 | 0.9124 | 0.8579 | 0.9735 |
| 0.0216 | 4.0 | 7828 | nan | 0.7913 | 0.8842 | 0.8352 | 0.9718 |
| 0.0175 | 5.0 | 9785 | nan | 0.7735 | 0.9248 | 0.8424 | 0.9721 |
| 0.0117 | 6.0 | 11742 | nan | 0.9206 | 0.9294 | 0.9250 | 0.9833 |
| 0.0121 | 7.0 | 13699 | nan | 0.8988 | 0.9318 | 0.9150 | 0.9819 |
| 0.0086 | 8.0 | 15656 | nan | 0.8922 | 0.9175 | 0.9047 | 0.9801 |
| 0.007 | 9.0 | 17613 | nan | 0.8482 | 0.8997 | 0.8732 | 0.9769 |
| 0.0051 | 10.0 | 19570 | nan | 0.8730 | 0.9274 | 0.8994 | 0.9798 |
| 0.0045 | 11.0 | 21527 | nan | 0.9172 | 0.9051 | 0.9111 | 0.9819 |
| 0.0014 | 12.0 | 23484 | nan | 0.9138 | 0.9155 | 0.9147 | 0.9823 |
| 0.0029 | 13.0 | 25441 | nan | 0.9099 | 0.9287 | 0.9192 | 0.9834 |
| 0.0035 | 14.0 | 27398 | nan | 0.9019 | 0.9294 | 0.9155 | 0.9831 |
| 0.0005 | 15.0 | 29355 | nan | 0.8886 | 0.9343 | 0.9109 | 0.9825 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Luciano/bertimbau-base-finetuned-brazilian_court_decisions
|
Luciano
| 2023-09-12T03:15:02Z | 150 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"pt",
"dataset:joelito/brazilian_court_decisions",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-13T10:45:34Z |
---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- joelito/brazilian_court_decisions
metrics:
- accuracy
train-eval-index:
- config: joelito--brazilian_court_decisions
task: text-classification
task_id: multi_class_classification
splits:
eval_split: test
col_mapping:
decision_description: text
judgment_label: target
widget:
- text: 'AGRAVO DE INSTRUMENTO. AÇÃO REVISIONAL DE CONTRATO. DEPÓSITO DO VALOR INCONTROVERSO.
IMPOSSIBILIDADE. NÃO PREENCHIMENTO DOS REQUISITOS PELO DEVEDOR. MANUTENÇÃO NA
POSSE DO BEM E VEDAÇÃO DE INSCRIÇÃO NOS ÓRGÃOS DE PROTEÇÃO AO CRÉDITO MEDIANTE
O DEPÓSITO DO VALOR ORIGINALMENTE CONTRATADO. 1. O autor requereu a sua manutenção
na posse do bem, o depósito em juízo dos valores que entende incontroversos, a
proibição de inscrição de seu nome nos órgãos de proteção ao crédito e a suspensão
de eventual ação de busca e apreensão do veículo. 2. O artigo 330, PARAGRAFO 2
e PARAGRAFO 3 do CODIGO DE PROCESSO CIVIL autoriza expressamente o depósito do
valor incontroverso nas ações revisionais de contrato mediante presença de três
requisitos concomitantes: a) propositura de ação contestando a existência integral
ou parcial do débito; b) efetiva demonstração de que a contestação da cobrança
indevida se funda na aparência do bom direito; e, c) que a parte efetue o depósito
do valor incontroverso ou preste caução idônea. Contudo, tal conduta não afastará
a incidência das restrições legais decorrentes da dívida, porquanto os valores
considerados incontroversos são inferiores àqueles contratados. 3. A jurisprudência
é pacífica no sentido de ser necessário o depósito do valor integral da parcela
da dívida para inibir os efeitos da mora e suas consequências. 4. Nos termos da
jurisprudência consolidada nesta Corte, deverá o devedor depositar os valores
pactuados originalmente no contrato, tanto para as parcelas vencidas, quanto para
as vincendas, nas datas pactuadas, se desejar se manter na posse do bem e obstacular
sua inscrição de seu nome nos cadastros restritivos de crédito.'
- text: O constrangimento ilegal deve ser aferido segundo as circunstancias do caso
concreto, especialmente diante da complexidade e das diligências necessárias,
ainda mais quando tais elementos são necessários para embasar a própria denúncia
em si. Intensa movimentação processual no sentido de angariar as informações necessárias.
Prazo que merece ser mitigado. Denúncia oferecida nos autos da ação penal 0800162-9820188020001.
II - Presentes a materialidade dos delitos e seus indícios de autoria, mormente
diante das colaborações premiadas colhidas durante as investigações, não há falar
em embasamento abstrato da decisão especialmente quando esta expressamente apontou
que o paciente era proprietário de empresas supostamente fantasmas, que recebiam
benefícios de diversas prefeituras. III - Ausência de constrangimento ilegal,
tanto pela presença dos requisitos para a prisão preventiva em questão, quanto
pelo fato de que resta evidenciado que o paciente ficou foragido. Precedentes.
IV - Especificamente em relação ao pedido de extensão dos efeitos dos benefícios
concedidos a outros investigados (Josimar Campos, Raphael de Barros Lima e Raulene
Karoline Barros e Gabriel Brandão), entendo, a partir do narrado pela própria
petição inicial, que a situação do paciente é diferente dos demais investigados,
uma vez que, além de ele ter foragido do distrito da culpa e responder a outras
ações penais, aparentemente não está colaborando com as investigações (os outros
investigados firmaram acordo de colaboração premiada). V - No que tange ao pedido
relacionado aos ex-prefeitos (Márcia Coutinho Nogueira de Albuquerque, José Jacob
Gomes Brandão, Fábio Rangel Nunes de Oliveira) que se encontram em liberdade,
entendo que a situação do paciente também é diferente, uma vez que a decisão de
origem da conta que existem indicativos de que o esquema tenha ocorrido em 80
(oitenta) prefeituras. VI - A partir da leitura da decisão prolatada pelos juízes
impetrados, verifica-se que o decreto de prisão não é fundamentado apenas no risco
de a liberdade do paciente atrapalhar as investigações, mas também, para, sobretudo,
garantir a aplicação da lei penal pelo fato de o paciente ter foragido do distrito
da culpa.
- text: APELAÇÃO CÍVEL. AÇÃO ORIGINÁRIA DE USUCAPIÃO. JULGAMENTO ANTECIPADO DA LIDE.
SENTENÇA DE IMPROCEDÊNCIA AO PLEITO AUTORAL ANTE A AUSÊNCIA DOS REQUISITOS DO
ARTIGO 1238 DO CÓDIGO CIVIL/02. AUSÊNCIA DE CITAÇÃO DO PROPRIETÁRIO DO BEM A SER
USUCAPIDO. AUTOR COMPROVA POR DOCUMENTO EXPEDIDO PELA MUNICIPALIDADE O REGISTRO
DO IMÓVEL USUCAPIENDO EM SEU NOME DESDE 1990. POSSIBILIDADE DO PREENCHIMENTO DO
REQUISITO TEMPORAL (PRAZO PARA USUCAPIR) PREVISTO EM LEI A SER IMPLEMENTADO NO
CURSO DA DEMANDA. OFENSA AO DEVIDO PROCESSO LEGAL MATÉRIA DE ORDEM PÚBLICA, RECONHECIDA
DE OFÍCIO NO JUÍZO AD QUEM. NULIDADE DA SENTENÇA COM RETORNO DOS AUTOS À ORIGEM
PARA DAR SEGUIMENTO A INSTRUÇÃO COM PROLAÇÃO DE NOVA DECISÃO.
base_model: neuralmind/bert-base-portuguese-cased
model-index:
- name: bertimbau-base-finetuned-brazilian_court_decisions
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: joelito/brazilian_court_decisions
type: joelito/brazilian_court_decisions
config: joelito--brazilian_court_decisions
split: test
metrics:
- type: accuracy
value: 0.7753086419753087
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2Q0N2I5ZThjOTdmYjJmNjQ2M2EwYzllOGZlZmUzMzQ3ZTNmMDYwN2Y4NTk3MjA4NTBiYzBkNGRmMDVjMWE0YiIsInZlcnNpb24iOjF9.azo0rnA6IBxWvLcVuY37hgCQ2Krss0pqrqzHJ_cu4y6hb5IHupoPUzvkAXpv5_U_iOVAq_xPS8Ow9CU9YLDjAg
- type: precision
value: 0.7250426732752088
name: Precision Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDFlNGMzM2VlMGNkOGYwNWU1N2U1NDVmNjlmMjZmYTI1YzVmNTJkZTVlMmY1NTQ3NDkzOWRlMjBmZDZlMDlkMiIsInZlcnNpb24iOjF9.2xet0XJ9AzIF4WvH-QPborSKwNYxrnjI88yYbx5rmt82Uw5_KutBG_LLHl-H7ZDLcBmbLrCGq9kAz7FsDR8DDg
- type: precision
value: 0.7753086419753087
name: Precision Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTRmYTY2MWZhY2ZlZjYwMmQ5NzBkZjNkYzZiMGU0NmI5OTA5MDZjMGZkNDczNzc1OWVjNDE0YzFlMDE3MjU5YyIsInZlcnNpb24iOjF9.2smiUGVwRxTXdTcWm9wT_7HfYMQBGtNGiRECC-VqDgJalFiJL8z42RhaL0PpX29P9Gs2AqHj_CC2yOpcF9-dBQ
- type: precision
value: 0.772642124493271
name: Precision Weighted
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGYwN2I2MzcwODBjZTI0MjRjZjZmYzg5OGQyYzI3MzNiNDg3OTRlOGY0ZjBhM2NkMzdmM2Q1MGY1OTg3NGQ4YyIsInZlcnNpb24iOjF9.alVSoPTd0sN2WnlwUsvjMB3FMwgq4idmBg61VvZEGGjMheq-gantO2LEjghOLvyqV2ykHKZ3HtsaBZg3MFh1BQ
- type: recall
value: 0.7165701681830714
name: Recall Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzdhMGFlOTk1ZDY5NmNkMjQ0ZDJjOWI1NzMxYmFiNTZiNzM3N2ZhY2ZmZGRkYjBlMmY1ZjM4MDAzYWRhZmYxYyIsInZlcnNpb24iOjF9.iOY4HQhYtb0j4Aj0Q1pohB39QNNzBfeF3KjMeN33hgaRrK5BgoL_6VH_g_-n7oY00Pmw5sXbaLdO7obCpQooAQ
- type: recall
value: 0.7753086419753087
name: Recall Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDFlY2ViMjdmM2MzNDVkNGVjM2UxMzk5N2MyOWM3NTE5ZjQ2ZTJjMGMzNDViNmYzNWZiN2E0MmM3MTFhZmJhNCIsInZlcnNpb24iOjF9.MrfbjuTh4fNTjeNz6SNJ9L5bAxOQYtfXFArg0lMN7dzji8BtpSep_pVwb-27A6Bem7V2xlc27PdCMayVu08oCw
- type: recall
value: 0.7753086419753087
name: Recall Weighted
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTFkOGJhODNjZWI3OWM2ZDQwNWNkNWViYjZmOWQ3NDAzNWIzMjM1ZmRlNThiMjhkYjhkMDM0ZGU5MTdhOWYyOSIsInZlcnNpb24iOjF9.sxK_3TdQnAcCttSHzVvPOGxGlOfpeKYBZ9z6rTEAUw2G6HlC09jcxhlcl5nQRvpfMcMAzgVTrL6X3kgRjB9VBg
- type: f1
value: 0.7202933364810833
name: F1 Macro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZkMzNmZjJmNDhjZWU3MWM3ODdjNDA1OTY3ZGY3MmJmM2VjOTk2YTdkYzk2NWYxMGVjYjNmZTA1YTAxYjdjZiIsInZlcnNpb24iOjF9.tJ0qIvWo2pz1nzxCx-nCXm9BQPP94VV7cOEVQLPE2U3YRgOuIFMO99CW4a_Ge70XPLyFTIRCbr2-xDpMrC03Cw
- type: f1
value: 0.7753086419753087
name: F1 Micro
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGJiZDA4ZWU3NzA3OWU0N2YzNTU1NTQ1MmI2MzdlNWMwYTFhYjk1ZTQ5NDA0NzIyYTEwYWU2NGYxMDg5MzE5NyIsInZlcnNpb24iOjF9.HrC1_am-qUC4HboPtIanE1np2faZVqSPy58VlY3oK-nTPHYyEt_6FfgJoP2M6roVGKLjiXDs7gVljplAAG1XBQ
- type: f1
value: 0.7736389495276883
name: F1 Weighted
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjgzYjc2Y2JjYTI0MDFhYWZmOGY1YTc5ZTQ1NzI3ZDUxNWY5NzdhZmIyYzE1MTA5NTEyOTA4NjFkMmQ4ODI5ZCIsInZlcnNpb24iOjF9.TCvj3Rqei3q2ajB6L9dqIxTyKXhNFQuQrU4ymc7kWnEyPuKgB9ty2JTqjeRYPWpY2RicABU9UFDXEhrdcfbKCw
- type: loss
value: 0.6030029058456421
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmUyZjdjMTNiOGQ4OGJmZTg3MjU2MjczNDY0OWNhODNjZTIzYjhhOWFmZWI2M2Q4NTI3MjU1YzcwOWNiMDI0MCIsInZlcnNpb24iOjF9.DZoUcmXU7RWW_AGR5ezU7ZbM9YobJ5nzQSgrtLVJtZjd6bELzCyafbsCCJE1Uia7Uz0HcW1B1J6mbev_z6TlAg
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertimbau-base-finetuned-brazilian_court_decisions
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6424
- Accuracy: 0.7921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 203 | 0.7726 | 0.6683 |
| No log | 2.0 | 406 | 0.5948 | 0.7673 |
| 0.7112 | 3.0 | 609 | 0.6424 | 0.7723 |
| 0.7112 | 4.0 | 812 | 0.6376 | 0.7772 |
| 0.3383 | 5.0 | 1015 | 0.6424 | 0.7921 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Luciano/gpt2-small-portuguese-finetuned-tcu-acordaos
|
Luciano
| 2023-09-12T03:14:44Z | 154 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"pt",
"base_model:pierreguillou/gpt2-small-portuguese",
"base_model:finetune:pierreguillou/gpt2-small-portuguese",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- pt
license: mit
tags:
- generated_from_trainer
base_model: pierreguillou/gpt2-small-portuguese
model-index:
- name: gpt2-small-portuguese-finetuned-tcu-acordaos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-small-portuguese-finetuned-tcu-acordaos
This model is a fine-tuned version of [pierreguillou/gpt2-small-portuguese](https://huggingface.co/pierreguillou/gpt2-small-portuguese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3435 | 1.0 | 658 | 1.8346 |
| 1.8668 | 2.0 | 1316 | 1.7141 |
| 1.7573 | 3.0 | 1974 | 1.6841 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Luciano/bertimbau-base-finetuned-lener-br
|
Luciano
| 2023-09-12T03:12:01Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"pt",
"dataset:Luciano/lener_br_text_to_lm",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-04T13:01:45Z |
---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- Luciano/lener_br_text_to_lm
widget:
- text: Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função
legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha
(a de legislador positivo), usurpando, desse modo, no contexto de um sistema de
poderes essencialmente limitados, competência que não lhe pertence, com evidente
transgressão ao princípio constitucional da separação de poderes.
- text: O autor sustenta que a lei é formal e materialmente inconstitucional, em violação
aos arts. 15, XIV e XV, 19, caput, 53, 71, §1º, I , e 100, VI e X, da Lei Orgânica
do DF, uma vez que, ( i ) originou-se de iniciativa parlamentar quando necessáriainiciativa
privativa do Chefe do Poder Executivo, suscitando, inclusive, violação ao postulado
constitucional da `` reserva de administração '', a qual impede a ingerência normativa
do Poder [MASK] em matérias de competência executiva ; ( ii ) autoriza a delegação
de poder de polícia , atividade típica e exclusiva de Estado , na medida em que
permite ao Distrito Federal firmar convênios com o Conselho Regional de Engenharia
e Agronomia do Distrito Federal - CREA-DF e com o Conselho de Arquitetura e Urbanismo
do Distrito Federal - CAU-DF para, por meio do seu corpo técnico e credenciados,
atuarem na análise de processos de concessão de Alvará de Construção e de Carta
de Habite-se.
base_model: neuralmind/bert-base-portuguese-cased
model-index:
- name: bertimbau-base-finetuned-lener-br
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertimbau-base-finetuned-lener-br
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the [Luciano/lener_br_text_to_lm](https://huggingface.co/datasets/Luciano/lener_br_text_to_lm) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8132
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3167 | 1.0 | 2079 | 1.1163 |
| 1.1683 | 2.0 | 4158 | 1.0594 |
| 1.0648 | 3.0 | 6237 | 1.0501 |
| 1.0228 | 4.0 | 8316 | 0.9693 |
| 0.9662 | 5.0 | 10395 | 0.9847 |
| 0.9422 | 6.0 | 12474 | 0.9556 |
| 0.8696 | 7.0 | 14553 | 0.8978 |
| 0.7856 | 8.0 | 16632 | nan |
| 0.7849 | 9.0 | 18711 | 0.9192 |
| 0.7559 | 10.0 | 20790 | 0.8536 |
| 0.7564 | 11.0 | 22869 | 0.9230 |
| 0.7641 | 12.0 | 24948 | 0.8852 |
| 0.7007 | 13.0 | 27027 | 0.8616 |
| 0.7139 | 14.0 | 29106 | 0.8419 |
| 0.6543 | 15.0 | 31185 | 0.8460 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
choz/distilbert-base-uncased-finetuned-squad
|
choz
| 2023-09-12T02:45:05Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-12T01:36:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1477
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2344 | 1.0 | 5533 | 1.1513 |
| 0.9611 | 2.0 | 11066 | 1.1156 |
| 0.7538 | 3.0 | 16599 | 1.1477 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
casque/GoodHands-beta2
|
casque
| 2023-09-12T02:41:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-12T01:22:33Z |
---
license: creativeml-openrail-m
---
|
haozhangphy/ppo-LunarLander-v2
|
haozhangphy
| 2023-09-12T02:36:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T02:36:37Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.75 +/- 18.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
kyuwon416/Reinforce-CartPole-v1
|
kyuwon416
| 2023-09-12T02:30:50Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T02:30:40Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Sambosis/autotrain-2ndtrain-88581143712
|
Sambosis
| 2023-09-12T02:26:04Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:Sambosis/autotrain-data-2ndtrain",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-09-12T02:17:18Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- Sambosis/autotrain-data-2ndtrain
co2_eq_emissions:
emissions: 0.09774058749638984
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 88581143712
- CO2 Emissions (in grams): 0.0977
## Validation Metrics
- Loss: 0.155
- Rouge1: 27.320
- Rouge2: 22.891
- RougeL: 25.972
- RougeLsum: 26.015
- Gen Len: 20.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Sambosis/autotrain-2ndtrain-88581143712
```
|
abhilad98/db_abhi
|
abhilad98
| 2023-09-12T02:22:19Z | 4 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2",
"base_model:adapter:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-11T21:41:55Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
instance_prompt: a photo of Abhi
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - abhilad98/db_abhi
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were trained on a photo of Abhi using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
minhbtc/q-FrozenLake-v1-4x4-noSlippery
|
minhbtc
| 2023-09-12T02:21:37Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T02:15:17Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="minhbtc/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
nanom/gtp_adaptation_martin_fierro_v2
|
nanom
| 2023-09-12T02:15:13Z | 134 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:DeepESP/gpt2-spanish",
"base_model:finetune:DeepESP/gpt2-spanish",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-12T01:23:34Z |
---
license: mit
base_model: DeepESP/gpt2-spanish
tags:
- generated_from_trainer
model-index:
- name: Martin-Fierro
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Martin-Fierro
This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0919 | 1.0 | 40 | 4.9569 |
| 4.7032 | 2.0 | 80 | 4.8517 |
| 4.4604 | 3.0 | 120 | 4.8015 |
| 4.2456 | 4.0 | 160 | 4.7786 |
| 4.2514 | 5.0 | 200 | 4.7721 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
shaowenchen/chinese-llama-2-13b-16k-gguf
|
shaowenchen
| 2023-09-12T02:10:04Z | 86 | 0 | null |
[
"gguf",
"meta",
"llama",
"llama-2",
"chinese",
"text-generation",
"zh",
"license:other",
"region:us"
] |
text-generation
| 2023-09-09T00:07:52Z |
---
inference: false
language:
- zh
license: other
model_creator: ziqingyang
model_link: https://huggingface.co/ziqingyang/chinese-llama-2-13b-16k
model_name: chinese-llama-2-13b-16k
model_type: llama
pipeline_tag: text-generation
quantized_by: shaowenchen
tasks:
- text2text-generation
tags:
- meta
- gguf
- llama
- llama-2
- chinese
---
## Provided files
| Name | Quant method | Size |
| ----------------------------------- | ------------ | ----- |
| chinese-llama-2-13b-16k.Q2_K.gguf | Q2_K | 5.2 G |
| chinese-llama-2-13b-16k.Q3_K.gguf | Q3_K | 6.0 G |
| chinese-llama-2-13b-16k.Q3_K_L.gguf | Q3_K_L | 6.6 G |
| chinese-llama-2-13b-16k.Q3_K_S.gguf | Q3_K_S | 5.4 G |
| chinese-llama-2-13b-16k.Q4_0.gguf | Q4_0 | 7.0 G |
| chinese-llama-2-13b-16k.Q4_1.gguf | Q4_1 | 7.8 G |
| chinese-llama-2-13b-16k.Q4_K.gguf | Q4_K | 7.5 G |
| chinese-llama-2-13b-16k.Q4_K_S.gguf | Q4_K_S | 7.1 G |
| chinese-llama-2-13b-16k.Q5_0.gguf | Q5_0 | 8.5 G |
| chinese-llama-2-13b-16k.Q5_1.gguf | Q5_1 | 9.3 G |
| chinese-llama-2-13b-16k.Q5_K.gguf | Q5_K | 8.8 G |
| chinese-llama-2-13b-16k.Q5_K_S.gguf | Q5_K_S | 8.5 G |
| chinese-llama-2-13b-16k.Q6_K.gguf | Q6_K | 10 G |
| chinese-llama-2-13b-16k.Q8_0.gguf | Q8_0 | 13 G |
| chinese-llama-2-13b-16k.gguf | full | 25 G |
Usage:
```
docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest
```
and you can view http://localhost:8000/docs to see the swagger UI.
|
polejowska/detr-r50-cd45rb-1ah-6l-corrected
|
polejowska
| 2023-09-12T01:52:29Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cd45rb_nan_xywh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-08-14T10:47:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cd45rb_nan_xywh
model-index:
- name: detr-r50-cd45rb-1ah-6l-corrected
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-r50-cd45rb-1ah-6l-corrected
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cd45rb_nan_xywh dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.9229 | 1.0 | 4606 | 1.3205 |
| 1.703 | 2.0 | 9212 | 1.2364 |
| 1.5976 | 3.0 | 13818 | 1.1736 |
| 1.5323 | 4.0 | 18424 | 1.1464 |
| 1.483 | 5.0 | 23030 | 1.1084 |
| 1.4431 | 6.0 | 27636 | 1.1037 |
| 1.4153 | 7.0 | 32242 | 1.0743 |
| 1.3898 | 8.0 | 36848 | 1.0755 |
| 1.3712 | 9.0 | 41454 | 1.0604 |
| 1.3616 | 10.0 | 46060 | 1.0556 |
| 2.807 | 11.0 | 50666 | 2.1286 |
| 2.6791 | 12.0 | 55272 | 2.1132 |
| 2.6331 | 13.0 | 59878 | 2.0794 |
| 2.6108 | 14.0 | 64484 | 2.0570 |
| 2.5818 | 15.0 | 69090 | 2.0455 |
| 2.5693 | 16.0 | 73696 | 2.0330 |
| 2.5446 | 17.0 | 78302 | 2.0361 |
| 2.5298 | 18.0 | 82908 | 2.0160 |
| 2.5098 | 19.0 | 87514 | 2.0395 |
| 2.4951 | 20.0 | 92120 | 2.0063 |
| 2.4913 | 21.0 | 96726 | 2.0016 |
| 2.4703 | 22.0 | 101332 | 1.9800 |
| 2.4643 | 23.0 | 105938 | 1.9811 |
| 2.4611 | 24.0 | 110544 | 1.9807 |
| 2.4568 | 25.0 | 115150 | 1.9721 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
manhtt-079/vipubmed-deberta-xsmall
|
manhtt-079
| 2023-09-12T01:51:39Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"deberta-v2",
"transformer",
"vietnamese",
"nlp",
"bert",
"deberta",
"deberta-v3",
"fill-mask",
"vi",
"dataset:VietAI/vi_pubmed",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-04-14T02:35:24Z |
---
language:
- vi
metrics:
- f1
pipeline_tag: fill-mask
license: mit
datasets:
- VietAI/vi_pubmed
tags:
- transformer
- vietnamese
- nlp
- bert
- deberta
- deberta-v3
---
# ViPubMedDeBERTa: A Vietnamese pretrained biomedical language representation model
## Model description
## Model variations
## How to use
You can use this model directly with a pipeline for masked language modeling:<br>
**_NOTE:_** The input text should be already word-segmented, you can use [Pyvi](https://github.com/trungtv/pyvi) (Python Vietnamese Core NLP Toolkit) to segment word before passing to the model.
```python
>>> from transformers import pipeline
>>> model = pipeline('fill-mask', model='manhtt-079/vipubmed-deberta-xsmall')
>>> text_with_mask = """Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ) . FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm . Phẫu_thuật được coi là phương_thức điều_trị tốt nhất , tiếp_theo là hóa_trị . Trong trường_hợp của chúng_tôi , [MASK] cắt bỏ không_thể thực_hiện được , do đó bệnh_nhân được hóa_trị hai dòng , sau đó là cấy_ghép tủy xương , sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên"""
>>> model(text_with_mask)
[{'score': 0.7800273299217224,
'token': 1621,
'token_str': 'phẫu_thuật',
'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, phẫu_thuật cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'},
{'score': 0.13326583802700043,
'token': 83,
'token_str': 'việc',
'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, việc cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'},
{'score': 0.014797757379710674,
'token': 589,
'token_str': 'phương_pháp',
'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, phương_pháp cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'},
{'score': 0.011190224438905716,
'token': 23339,
'token_str': 'sinh_thiết',
'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, sinh_thiết cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'},
{'score': 0.006846326868981123,
'token': 454,
'token_str': 'điều_trị',
'sequence': 'Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS ). FDCS là bệnh rất hiếm ảnh_hưởng đến tế_bào trình_diện kháng_nguyên đuôi gai và thường bị chẩn_đoán nhầm. Phẫu_thuật được coi là phương_thức điều_trị tốt nhất, tiếp_theo là hóa_trị. Trong trường_hợp của chúng_tôi, điều_trị cắt bỏ không_thể thực_hiện được, do đó bệnh_nhân được hóa_trị hai dòng, sau đó là cấy_ghép tủy xương, sau đó là hóa_trị ba với đáp_ứng trao_đổi chất hoàn_toàn được thấy trên'}]
```
#### Get features:
- With PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('manhtt-079/vipubmed-deberta-xsmall')
model = AutoModel.from_pretrained("manhtt-079/vipubmed-deberta-xsmall")
text = "Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS )."
model_inputs = tokenizer(text, return_tensors='pt')
outputs = model(**model_inputs)
```
- With TensorFlow
```python
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained('manhtt-079/vipubmed-deberta-xsmall')
model = TFAutoModel.from_pretrained("manhtt-079/vipubmed-deberta-xsmall")
text = "Chúng_tôi mô_tả một trường_hợp bệnh_nhân nữ 44 tuổi được chẩn_đoán sarcoma tế_bào tua nang ( FDCS )."
model_inputs = tokenizer(text, return_tensors='tf')
outputs = model(**model_inputs)
```
## Pre-training data
The ViPubMedDeBERTa model was pre-trained on [ViPubmed](https://github.com/vietai/ViPubmed), a dataset consisting of 20M Vietnamese Biomedical abstracts generated by large scale translation.
## Training procedure
### Data deduplication
A fuzzy deduplication, targeting documents with high overlap, was conducted at the document level to enhance quality and address overfitting. Employing Locality Sensitive Hashing (LSH) with a threshold of 0.9 ensured the removal of documents with overlap exceeding 90%. This process resulted in an average reduction of the dataset's size by 3%.
### Pretraining
We employ our model based on the [ViDeBERTa](https://github.com/HySonLab/ViDeBERTa) architecture and leverage its pre-trained checkpoint to continue pre-training. Our model was trained on a single A100 GPU (40GB) for 220 thousand steps, with a batch size of 24 and gradient accumulation steps set to 4 (resulting in a total of 96). The sequence length was limited to 512 tokens and the model peak learning rate of 1e-4.
## Evaluation results
|
joey234/whisper-small-vi
|
joey234
| 2023-09-12T01:44:50Z | 94 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"vi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-03T00:11:03Z |
---
language:
- vi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small Vietnamese
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 vi
type: mozilla-foundation/common_voice_11_0
config: vi
split: None
metrics:
- type: wer
value: 34.21715788320368
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Vietnamese
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 vi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9921
- Wer: 34.2172
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0002 | 124.0 | 1000 | 0.7998 | 21.7706 |
| 0.0001 | 249.0 | 2000 | 0.8833 | 28.9690 |
| 0.0 | 374.0 | 3000 | 0.9382 | 30.8206 |
| 0.0 | 499.0 | 4000 | 0.9754 | 34.4363 |
| 0.0 | 624.0 | 5000 | 0.9921 | 34.2172 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
nikolasmoya/c4-binary-english-grammar-checker
|
nikolasmoya
| 2023-09-12T01:31:47Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-01T22:02:26Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- matthews_correlation
model-index:
- name: c4-binary-english-grammar-checker
results: []
---
# Usage instructions:
The recommendation is to split the text into sentences and evaluate sentence by sentence, you can do that using spacy:
```
import spacy
def clean_up_sentence(text: str) -> str:
text = text.replace("---", "")
text = text.replace("\n", " ")
text = text.strip()
if not text.endswith(('.', '!', '?', ":")):
# Since we are breaking a longer text into sentences ourselves, we should always end a sentence with a period.
text = text + "."
return text
sentence_splitter = spacy.load("en_core_web_sm")
spacy_document = sentence_splitter("This is a long text. It has two or more sentence. Spacy will break it down into sentences.")
results = []
for sentence in spacy_document.sents:
clean_text = clean_up_sentence(str(sentence))
classification = grammar_checker(clean_text)[0]
results.append({
"label": classification['label'],
"score": classification['score'],
"sentence": clean_text
})
pd.DataFrame.from_dict(results)
```
# c4-binary-english-grammar-checker
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3546
- Accuracy: 0.8577
- Matthews Correlation: 0.7192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------------------:|
| 0.363 | 1.0 | 200000 | 0.3634 | 0.8487 | 0.7025 |
| 0.3032 | 2.0 | 400000 | 0.3546 | 0.8577 | 0.7192 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
chunwoolee0/klue_ynat_roberta_base_model
|
chunwoolee0
| 2023-09-12T01:28:58Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"arxiv:2105.09680",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-18T14:43:31Z |
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: klue_ynat_roberta_base_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
config: ynat
split: validation
args: ynat
metrics:
- name: F1
type: f1
value: 0.872014500465787
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# klue_ynat_roberta_base_model
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3747
- F1: 0.8720
## Model description
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## Intended uses & limitations
Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.
## Training and evaluation data
## How to use
_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 179 | 0.4838 | 0.8444 |
| No log | 2.0 | 358 | 0.3848 | 0.8659 |
| 0.4203 | 3.0 | 537 | 0.3778 | 0.8690 |
| 0.4203 | 4.0 | 716 | 0.3762 | 0.8702 |
| 0.4203 | 5.0 | 895 | 0.3747 | 0.8720 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
chunwoolee0/klue_ner_roberta_model
|
chunwoolee0
| 2023-09-12T01:28:23Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:klue",
"arxiv:2105.09680",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-19T15:24:39Z |
---
base_model: klue/roberta-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_ner_roberta_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.9545986426398315
- name: Recall
type: recall
value: 0.9557169634489222
- name: F1
type: f1
value: 0.955157475705421
- name: Accuracy
type: accuracy
value: 0.9883703228112445
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# klue_ner_roberta_model
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0487
- Precision: 0.9546
- Recall: 0.9557
- F1: 0.9552
- Accuracy: 0.9884
## Model description
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## Intended uses & limitations
## How to use
_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
```
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0449 | 1.0 | 2626 | 0.0601 | 0.9361 | 0.9176 | 0.9267 | 0.9830 |
| 0.0262 | 2.0 | 5252 | 0.0469 | 0.9484 | 0.9510 | 0.9497 | 0.9874 |
| 0.0144 | 3.0 | 7878 | 0.0487 | 0.9546 | 0.9557 | 0.9552 | 0.9884 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
souljoy/distilhubert-finetuned-gtzan
|
souljoy
| 2023-09-12T01:23:47Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-12T00:56:22Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-1epoch
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.53
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan-1epoch
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8456
- Accuracy: 0.53
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0099 | 1.0 | 113 | 1.8456 | 0.53 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
chunwoolee0/klue_nli_roberta_base_model
|
chunwoolee0
| 2023-09-12T01:23:41Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-18T05:01:52Z |
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- accuracy
model-index:
- name: klue_nli_roberta_base_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
config: nli
split: validation
args: nli
metrics:
- name: Accuracy
type: accuracy
value: 0.8653333333333333
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# klue_nli_roberta_base_model
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base)
on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6867
- Accuracy: 0.8653
## Model description
Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.
## Intended uses & limitations
## How to use
*NOTE*: Use BertTokenizer instead of RobertaTokenizer. (AutoTokenizer will load BertTokenizer)
from transformers import AutoModel, AutoTokenizer
```python
model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
```
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5988 | 1.0 | 782 | 0.4378 | 0.8363 |
| 0.2753 | 2.0 | 1564 | 0.4169 | 0.851 |
| 0.1735 | 3.0 | 2346 | 0.5267 | 0.8607 |
| 0.0956 | 4.0 | 3128 | 0.6275 | 0.8683 |
| 0.0708 | 5.0 | 3910 | 0.6867 | 0.8653 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
deepachalapathi/msrc_2
|
deepachalapathi
| 2023-09-12T01:23:29Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-09-12T01:23:13Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# whateverweird17/msrc_2
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("whateverweird17/msrc_2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
miscjose/mt5-small-finetuned-genius-music
|
miscjose
| 2023-09-12T01:12:25Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"music",
"song-lyrics",
"dataset:miscjose/genius-music",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-08-09T02:25:18Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
- music
- song-lyrics
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-genius
results: []
pipeline_tag: summarization
datasets:
- miscjose/genius-music
widget:
- text: >
Thought I'd end up with Sean
But he wasn't a match
Wrote some songs about Ricky
Now I listen and laugh
Even almost got married
And for Pete, I'm so thankful
Wish I could say, "Thank you" to Malcolm
'Cause he was an angel
One taught me love
One taught me patience
And one taught me pain
Now, I'm so amazing
Say I've loved and I've lost
But that's not what I see
So, look what I got
Look what you taught me
And for that, I say
Thank you, next (Next)
Thank you, next (Next)
Thank you, next
I'm so fuckin' grateful for my ex
Thank you, next (Next)
Thank you, next (Next)
Thank you, next (Next)
I'm so fuckin'—
Spend more time with my friends
I ain't worried 'bout nothin'
Plus, I met someone else
We havin' better discussions
I know they say I move on too fast
But this one gon' last
'Cause her name is Ari
And I'm so good with that (So good with that)
She taught me love (Love)
She taught me patience (Patience)
How she handles pain (Pain)
That shit's amazing (Yeah, she's amazing)
I've loved and I've lost (Yeah, yeah)
But that's not what I see (Yeah, yeah)
'Cause look what I've found (Yeah, yeah, I've found)
Ain't no need for searching, and for that, I say
Thank you, next (Thank you, next)
Thank you, next (Thank you, next)
Thank you, next (Thank you)
I'm so fuckin' grateful for my ex
Thank you, next (Thank you, next)
Thank you, next (Said thank you, next)
Thank you, next (Next)
I'm so fuckin' grateful for my ex
Thank you, next
Thank you, next
Thank you, next
I'm so fuckin'—
One day I'll walk down the aisle
Holding hands with my mama
I'll be thanking my dad
'Cause she grew from the drama
Only wanna do it once, real bad
Gon' make that shit last
God forbid something happens
Least this song is a smash (Song is a smash)
I've got so much love (Love)
Got so much patience (Patience)
And I've learned from the pain (Pain)
I turned out amazing (Turned out amazing)
Say I've loved and I've lost (Yeah, yeah)
But that's not what I see (Yeah, yeah)
'Cause look what I've found (Yeah, yeah)
Ain't no need for searching
And for that, I say
Thank you, next (Thank you, next)
Thank you, next (Thank you, next)
Thank you, next
I'm so fuckin' grateful for my ex
Thank you, next (Thank you, next)
Thank you, next (Said thank you, next)
Thank you, next (Next)
I'm so fuckin' grateful for my ex
Thank you, next
Thank you, next
Thank you, next
Yeah, yee
Thank you, next
Thank you, next
Thank you, next
Yeah, yee
- text: >
Who's this?
Da-ddy Yan-kee!
Zúmbale mambo pa' que mis gatas prendan los motore'
Zúmbale mambo pa' que mis gatas prendan los motore'
Zúmbale mambo pa' que mis gatas prendan los motore'
Que se preparen que lo que viene es pa' que le den
¡Duro! Mamita, yo sé que tú no te me va' a quitar
¡Duro! Lo que me gusta es que tú te dejas llevar
¡Duro! Todos los weekend'es ella sale a vacilar
¡Duro! Mi gata no para 'e janguear, porque
A ella le gusta la gasolina (Dame más gasolina)
Cómo le encanta la gasolina (Dame más gasolina)
A ella le gusta la gasolina (Dame más gasolina)
Cómo le encanta la gasolina (Dame más gasolina)
Ella prende las turbinas, no discrimina
No se pierde ni un party de marquesina
Se acicala hasta pa' la esquina
Luce tan bien que hasta la sombra le combina
Asesina, me domina
Janguea en carros, motoras y limusinas
Llena su tanque de adrenalina
Cuando escucha reggaetón en las bocinas
A ella le gusta la gasolina (Dame más gasolina)
Cómo le encanta la gasolina (Dame más gasolina)
A ella le gusta la gasolina (Dame más gasolina)
Cómo le encanta la gasolina (Dame más gasolina)
Aquí somos los mejores, no te me ajore'
En la pista nos llaman "Los Matadore'"
Tú hace' que cualquiera se enamore
Cuando bailas al ritmo de los tambore'
Esto va pa' las gatas de to's colore'
Pa' las mayore', pa' las menore'
Pa' las que son más zorras que los cazadore'
Pa' las mujeres que no apagan sus motore'
Tenemo' tú y yo algo pendiente
Tú me debes algo y lo sabe'
Conmigo ella se pierde
No le rinde cuentas a nadie
Tenemo' tú y yo algo pendiente
Tú me debes algo y lo sabe'
Conmigo ella se pierde
No le rinde cuentas a nadie
Zúmbale mambo pa' que mis gatas prendan los motore'
Zúmbale mambo pa' que mis gatas prendan los motore'
Zúmbale mambo pa' que mis gatas prendan los motore'
Que se preparen que lo que viene es pa' que le den
¡Duro! Mamita, yo sé que tú no te me va' a quitar
¡Duro! Lo que me gusta es que tú te dejas llevar
¡Duro! Todos los weekend'es ella sale a vacilar
¡Duro! Mi gata no para 'e janguear, porque
A ella le gusta la gasolina (Dame más gasolina)
Cómo le encanta la gasolina (Dame más gasolina)
A ella le gusta la gasolina (Dame más gasolina)
Cómo le encanta la gasolina (Dame más gasolina)
- text: >
無敵の笑顔で荒らすメディア
知りたいその秘密ミステリアス
抜けてるとこさえ彼女のエリア
完璧で嘘つきな君は
天才的なアイドル様
今日何食べた？
好きな本は？
遊びに行くならどこに行くの？
何も食べてない
それは内緒
何を聞かれても
のらりくらり
そう淡々と
だけど燦々と
見えそうで見えない秘密は蜜の味
あれもないないない
これもないないない
好きなタイプは？
相手は？
さあ答えて
「誰かを好きになることなんて私分からなくてさ」
嘘か本当か知り得ない
そんな言葉にまた一人堕ちる
また好きにさせる
誰もが目を奪われていく
君は完璧で究極のアイドル
金輪際現れない
一番星の生まれ変わり
その笑顔で愛してるで
誰も彼も虜にしていく
その瞳がその言葉が
嘘でもそれは完全なアイ
はいはいあの子は特別です
我々はハナからおまけです
お星様の引き立て役Bです
全てがあの子のお陰なわけない
洒落臭い
妬み嫉妬なんてないわけがない
これはネタじゃない
からこそ許せない
完璧じゃない君じゃ許せない
自分を許せない
誰よりも強い君以外は認めない
誰もが信じ崇めてる
まさに最強で無敵のアイドル
弱点なんて見当たらない
一番星を宿している
弱いとこなんて見せちゃダメダメ
知りたくないとこは見せずに
唯一無二じゃなくちゃイヤイヤ
それこそ本物のアイ
得意の笑顔で沸かすメディア
隠しきるこの秘密だけは
愛してるって嘘で積むキャリア
これこそ私なりの愛だ
流れる汗も綺麗なアクア
ルビーを隠したこの瞼
歌い踊り舞う私はマリア
そう嘘はとびきりの愛だ
誰かに愛されたことも
誰かのこと愛したこともない
そんな私の嘘がいつか本当になること
信じてる
いつかきっと全部手に入れる
私はそう欲張りなアイドル
等身大でみんなのこと
ちゃんと愛したいから
今日も嘘をつくの
この言葉がいつか本当になる日を願って
それでもまだ
君と君にだけは言えずにいたけど
やっと言えた
これは絶対嘘じゃない
愛してる
---
# mt5-small-finetuned-genius
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [Genius](https://genius.com/) Music dataset found [here](https://www.cs.cornell.edu/~arb/data/genius-expertise/).
The song lyrics and song titles were preprocessed and used for fine-tuning.
You can view more examples of this model's inference on the following [Space](https://huggingface.co/spaces/miscjose/song-title-generation).
## Model description
Please visit: [google/mt5-small](https://huggingface.co/google/mt5-small)
## Intended uses & limitations
- Intended Uses: Given song lyrics, generate a summary.
- Limitations: Due to the nature of music, the model can generate summaries containing hate speech.
## Training and evaluation data
- 27.6K Training Samples
- 3.45 Validation Samples
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 7.9304 | 1.0 | 863 | 3.5226 | 14.235 | 6.78 | 14.206 | 14.168 |
| 3.8394 | 2.0 | 1726 | 3.0382 | 22.97 | 13.166 | 22.981 | 22.944 |
| 3.3799 | 3.0 | 2589 | 2.9010 | 24.932 | 14.54 | 24.929 | 24.919 |
| 3.2204 | 4.0 | 3452 | 2.8441 | 26.678 | 15.587 | 26.624 | 26.665 |
| 3.1498 | 5.0 | 4315 | 2.8363 | **26.827** | **15.696** | **26.773** | **26.793** |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3
|
zayedupal/movie-genre-prediction_distilbert-base-uncased
|
zayedupal
| 2023-09-12T00:59:51Z | 116 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-14T06:44:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: movie-genre-prediction_distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# movie-genre-prediction_distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5869
- Accuracy: 0.4489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 200
- eval_batch_size: 200
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 163 | 1.6259 | 0.4358 |
| No log | 2.0 | 326 | 1.5705 | 0.4478 |
| No log | 3.0 | 489 | 1.5566 | 0.4527 |
| 1.6342 | 4.0 | 652 | 1.5656 | 0.4537 |
| 1.6342 | 5.0 | 815 | 1.5721 | 0.4504 |
| 1.6342 | 6.0 | 978 | 1.5825 | 0.4491 |
| 1.3404 | 7.0 | 1141 | 1.5869 | 0.4489 |
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
zayedupal/movie-genre-prediction_bert-base-uncased
|
zayedupal
| 2023-09-12T00:59:41Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-11T02:02:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: bert-base-uncased
model-index:
- name: movie-genre-prediction_bert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# movie-genre-prediction_bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6356
- Accuracy: 0.4201
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 200
- eval_batch_size: 200
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 189 | 1.6633 | 0.4130 |
| No log | 2.0 | 378 | 1.6356 | 0.4201 |
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
actionpace/Marcoroni-13B
|
actionpace
| 2023-09-12T00:56:09Z | 1 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-12T00:07:17Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* Marcoroni-13B_Q4_K_M.gguf
* Marcoroni-13B_Q5_K_M.gguf
**Source:** [AIDC-ai-business](https://huggingface.co/AIDC-ai-business)
**Source Model:** [Marcoroni-13B](https://huggingface.co/AIDC-ai-business/Marcoroni-13B)
**Source models for AIDC-ai-business/Marcoroni-13B (Finetune)**
- [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ([Ref](https://huggingface.co/actionpace/Llama-2-13b-hf))
|
shaowenchen/chinese-alpaca-2-7b-16k-gguf
|
shaowenchen
| 2023-09-12T00:52:17Z | 28 | 0 | null |
[
"gguf",
"meta",
"llama",
"llama-2",
"chinese",
"text-generation",
"en",
"license:other",
"region:us"
] |
text-generation
| 2023-09-08T04:50:45Z |
---
inference: false
language:
- en
license: other
model_creator: ziqingyang
model_link: https://huggingface.co/ziqingyang/chinese-alpaca-2-7b-16k
model_name: chinese-alpaca-2-7b-16k
model_type: llama
pipeline_tag: text-generation
quantized_by: shaowenchen
tasks:
- text2text-generation
tags:
- meta
- gguf
- llama
- llama-2
- chinese
---
## Provided files
| Name | Quant method | Size |
| ----------------------------------- | ------------ | ------ |
| chinese-alpaca-2-7b-16k.Q2_K.gguf | Q2_K | 2.7 GB |
| chinese-alpaca-2-7b-16k.Q3_K.gguf | Q3_K | 3.2 GB |
| chinese-alpaca-2-7b-16k.Q3_K_L.gguf | Q3_K_L | 3.5 GB |
| chinese-alpaca-2-7b-16k.Q3_K_S.gguf | Q3_K_S | 2.9 GB |
| chinese-alpaca-2-7b-16k.Q4_0.gguf | Q4_0 | 3.7 GB |
| chinese-alpaca-2-7b-16k.Q4_1.gguf | Q4_1 | 4.1 GB |
| chinese-alpaca-2-7b-16k.Q4_K.gguf | Q4_K | 3.9 GB |
| chinese-alpaca-2-7b-16k.Q4_K_S.gguf | Q4_K_S | 3.7 GB |
| chinese-alpaca-2-7b-16k.Q5_0.gguf | Q5_0 | 4.5 GB |
| chinese-alpaca-2-7b-16k.Q5_1.gguf | Q5_1 | 4.9 GB |
| chinese-alpaca-2-7b-16k.Q5_K.gguf | Q5_K | 4.6 GB |
| chinese-alpaca-2-7b-16k.Q5_K_S.gguf | Q5_K_S | 4.5 GB |
| chinese-alpaca-2-7b-16k.Q6_K.gguf | Q6_K | 5.3 GB |
| chinese-alpaca-2-7b-16k.Q8_0.gguf | Q8_0 | 6.9 GB |
| chinese-alpaca-2-7b-16k.gguf | full | 13 GB |
Usage:
```
docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest
```
and you can view http://localhost:8000/docs to see the swagger UI.
## Provided images
| Name | Quant method | Size |
| ------------------------------------------------- | ------------ | ------- |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q2_K` | Q2_K | 3.68 GB |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q3_K` | Q3_K | 4.16 GB |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q3_K_L` | Q3_K_L | 4.46 GB |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q3_K_S` | Q3_K_S | 3.81 GB |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q4_0` | Q4_0 | 4.7 GB |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q4_K` | Q4_K | 4.95 GB |
| `shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q4_K_S` | Q4_K_S | 4.73 GB |
Usage:
```
docker run --rm -p 8000:8000 shaowenchen/chinese-alpaca-2-7b-16k-gguf:Q2_K
```
and you can view http://localhost:8000/docs to see the swagger UI.
|
WGNW/Llama-2-ko-7b-Chat-auto-gptq-4bit-peft-remon
|
WGNW
| 2023-09-12T00:47:14Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T00:47:07Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.01
- desc_act: False
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.6.0.dev0
|
WGNW/tmp_chat_peft
|
WGNW
| 2023-09-12T00:42:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T00:41:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.01
- desc_act: False
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.6.0.dev0
|
geardriven/gearybooRVC
|
geardriven
| 2023-09-12T00:25:21Z | 0 | 1 | null |
[
"license:openrail",
"region:us"
] | null | 2023-09-11T10:19:40Z |
---
license: openrail
---
this is where i put my ai voices for my music covers.
yeah
yipeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee
|
AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2
|
AmelieSchreiber
| 2023-09-12T00:08:35Z | 128 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"esm",
"token-classification",
"biology",
"esm2",
"ESM-2",
"protein language model",
"en",
"dataset:AmelieSchreiber/general_binding_sites",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-06T07:36:43Z |
---
widget:
- text: "MEPLDDLDLLLLEEDSGAEAVPRMEILQKKADAFFAETVLSRGVDNRYLVLAVETKLNERGAEEKHLLITVSQEGEQEVLCILRNGWSSVPVEPGDIIHIEGDCTSEPWIVDDDFGYFILSPDMLISGTSVASSIRCLRRAVLSETFRVSDTATRQMLIGTILHEVFQKAISESFAPEKLQELALQTLREVRHLKEMYRLNLSQDEVRCEVEEYLPSFSKWADEFMHKGTKAEFPQMHLSLPSDSSDRSSPCNIEVVKSLDIEESIWSPRFGLKGKIDVTVGVKIHRDCKTKYKIMPLELKTGKESNSIEHRGQVILYTLLSQERREDPEAGWLLYLKTGQMYPVPANHLDKRELLKLRNQLAFSLLHRVSRAAAGEEARLLALPQIIEEEKTCKYCSQMGNCALYSRAVEQVHDTSIPEGMRSKIQEGTQHLTRAHLKYFSLWCLMLTLESQSKDTKKSHQSIWLTPASKLEESGNCIGSLVRTEPVKRVCDGHYLHNFQRKNGPMPATNLMAGDRIILSGEERKLFALSKGYVKRIDTAAVTCLLDRNLSTLPETTLFRLDREEKHGDINTPLGNLSKLMENTDSSKRLRELIIDFKEPQFIAYLSSVLPHDAKDTVANILKGLNKPQRQAMKKVLLSKDYTLIVGMPGTGKTTTICALVRILSACGFSVLLTSYTHSAVDNILLKLAKFKIGFLRLGQSHKVHPDIQKFTEEEMCRLRSIASLAHLEELYNSHPVVATTCMGISHPMFSRKTFDFCIVDEASQISQPICLGPLFFSRRFVLVGDHKQLPPLVLNREARALGMSESLFKRLERNESAVVQLTIQYRMNRKIMSLSNKLTYEGKLECGSDRVANAVITLPNLKDVRLEFYADYSDNPWLAGVFEPDNPVCFLNTDKVPAPEQIENGGVSNVTEARLIVFLTSTFIKAGCSPSDIGIIAPYRQQLRTITDLLARSSVGMVEVNTVDKYQGRDKSLILVSFVRSNEDGTLGELLKDWRRLNVAITRAKHKLILLGSVSSLKRF"
example_title: "Protein Sequence 1"
- text: "MNSVTVSHAPYYIVYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPPKFFIQLKQMLRNKRVCVCGILPYPIDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFEIINELLELDNKVPINWAQGFIY"
example_title: "Protein Sequence 2"
- text: "MNSVTVSHAPYTIAYHDDWEPVMSQLVEFYNEAASWLLRDETSPIPSKFNIQLKQPLRNKRVCVFGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLMGVIDYEGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPSARDRQFEKDRSFEIINVLLELDNKVPLNWAQGFIY"
example_title: "Protein Sequence 3"
license: mit
datasets:
- AmelieSchreiber/general_binding_sites
language:
- en
metrics:
- precision
- recall
- f1
library_name: transformers
tags:
- biology
- esm
- esm2
- ESM-2
- protein language model
---
# ESM-2 for General Protein Binding Site Prediction
This model is trained to predict general binding sites of proteins using only the sequence. This is a finetuned version of
`esm2_t6_8M_UR50D` ([see here](https://huggingface.co/facebook/esm2_t6_8M_UR50D) and [also here](https://huggingface.co/docs/transformers/model_doc/esm)
for more info on the base model), trained on [this dataset](https://huggingface.co/datasets/AmelieSchreiber/general_binding_sites). The data is
not filtered by family, and thus the model may be overfit to some degree. In the Hugging Face Inference API widget to the right
there are three protein sequence examples. The first is a DNA binding protein truncated to the first 1022 amino acid residues
([see UniProt entry here](https://www.uniprot.org/uniprotkb/D3ZG52/entry)).
The second and third were obtained using [EvoProtGrad](https://github.com/Amelie-Schreiber/sampling_protein_language_models/blob/main/EvoProtGrad_copy.ipynb)
a Markov Chain Monte Carlo method of (*in silico*) directed evolution of proteins based on a form of Gibbs sampling. The mutatant-type
protein sequences in theory should have similar binding sites to the wild-type protein sequence, but perhaps with higher binding affinity.
Testing this out on the model, we see the two proteins indeed have the same binding sites, which validates to some degree that the model
has learned to predict binding sites well (and that EvoProtGrad works as intended).
## Training
This model was trained on approximately 70,000 proteins with binding site and active site annotations in UniProt.
The training split was a random 85/15 split for this version, and does not consider anything in the way of family or sequence
similarity. New iterations of the model have been trained on larger datasets (over 200,000 proteins), with the split such that
there are no overlapping families, however they seem to overfit much earlier and have significantly worse performance in terms
of the training metrics (precision, recall, and F1). To address this we plan to implement LoRA (and hopefully QLoRA).
Training Metrics for the Model in the form of the `trainer_state.json` can be
[found here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2/blob/main/trainer_state.json).
```
epoch 3:
Training Loss Validation Loss Precision Recall F1 Auc
0.031100 0.074720 0.684798 0.966856 0.801743 0.980853
```
The hyperparameters are:
```
wandb: lr: 0.0004977045729600779
wandb: lr_scheduler_type: cosine
wandb: max_grad_norm: 0.5
wandb: num_train_epochs: 3
wandb: per_device_train_batch_size: 8
wandb: weight_decay: 0.025
```
## Using the Model
To use the model, try running:
```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
def predict_binding_sites(model_path, protein_sequences):
"""
Predict binding sites for a collection of protein sequences.
Parameters:
- model_path (str): Path to the saved model.
- protein_sequences (List[str]): List of protein sequences.
Returns:
- List[List[str]]: Predicted labels for each sequence.
"""
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForTokenClassification.from_pretrained(model_path)
# Ensure model is in evaluation mode
model.eval()
# Tokenize sequences
inputs = tokenizer(protein_sequences, return_tensors="pt", padding=True, truncation=True)
# Move to the same device as model and obtain logits
with torch.no_grad():
logits = model(**inputs).logits
# Obtain predicted labels
predicted_labels = torch.argmax(logits, dim=-1).cpu().numpy()
# Convert label IDs to human-readable labels
id2label = model.config.id2label
human_readable_labels = [[id2label[label_id] for label_id in sequence] for sequence in predicted_labels]
return human_readable_labels
# Usage:
model_path = "AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2" # Replace with your model's path
unseen_proteins = [
"MKVEEILEKALELVIPDEEEVRKGREAEEELRRRLDELGVEYVFVGSYARNTWLKGSLEIDVFLLFPEEFSKEELRERGLEIGKAVLDSYEIRYAEHPYVHGVVKGVEVDVVPCYKLKEPKNIKSAVDRTPFHHKWLEGRIKGKENEVRLLKGFLKANGIYGAEYKVRGFSGYLCELLIVFYGSFLETVKNARRWTRRTVIDVAKGEVRKGEEFFVVDPVDEKRNVAANLSLDNLARFVHLCREFMEAPSLGFFKPKHPLEIEPERLRKIVEERGTAVFAVKFRKPDIVDDNLYPQLERASRKIFEFLERENFMPLRSAFKASEEFCYLLFECQIKEISRVFRRMGPQFEDERNVKKFLSRNRAFRPFIENGRWWAFEMRKFTTPEEGVRSYASTHWHTLGKNVGESIREYFEIISGEKLFKEPVTAELCEMMGVKD",
"MKVEEILEKALELVIPDEEEVRKGREAEEELRRRLDELGVEYVFVGSYARNTWLKGSLEIAVFLLFPEEFSKEELRERGLEIGKAVLDSYEIRYAEHPYVHGVVKGVEVDVVPCYKLKEPKNIKSAVDRTPFHHKWLEGRIKGKENEVRLLKGFLKANGIYGAEYKVRGFSGYLCELLIVFYGSFLETVKNARRWTRRTVIDVAKGEVRKGEEFFVVDPVDEKRNVAANLSLDNLARFVHLCREFMEAPSLGFFKVKHPLEIEPERLRKIVEERGTAVFAVKFRKPDIVDDNLYPQLERASRKIFEFLERENFMPLRSAFKASEEFCYLLFECQIKEISRVFRRMGPQFEDERNVKKFLSRNRAFRPFIENGRWWAFEMRKFTTPEEGVRSYASTHWHTLGKNVGESIREYFEIISGEKLFKEPVTAELCEMMGVKD",
"MKVEEILEKALELVIPDEEEVRKGREAEEELRRRLDELGVEAVFVGSYARNTWLKGSLEIAVFLLFPEEFSKEELRERGLEIEKAVLDSYEIRYAEHPYVHGVVKGVEVDVVPCYKLKEPKNIKSAVDRTPFHHKELEGRIKGKENEVRLLKGFLKANGIYGAEYAVRGFSGYLCELLIVFYGSFLETVKNARRWTRRTVIDVAKGEVRKGEEFFVVDPVDEKRNVAANLSLDNLARFVHLCREFMEAPSLGFFKVKHPLEIEPERLRKIVEERGTAVFMVKFRKPDIVDDNLYPQLRRASRKIFEFLERNNFMPLRSAFKASEEFCYLLFECQIKEISDVFRRMGPLFEDERNVKKFLSRNRALRPFIENGRWWIFEMRKFTTPEEGVRSYASTHWHTLGKNVGESIREYFEIISGEKLFKEPVTAELCRMMGVKD",
"MKVEEILEKALELVIPDEEEVRKGREAEEELRRRLDELGVEAVFVGSYARNTWLKGSLEIAVFLLFPEEFSKEELRERGLEIEKAVLDSYGIRYAEHPYVHGVVKGVELDVVPCYKLKEPKNIKSAVDRTPFHHKELEGRIKGKENEYRSLKGFLKANGIYGAEYAVRGFSGYLCELLIVFYGSFLETVKNARRWTRKTVIDVAKGEVRKGEEFFVVDPVDEKRNVAALLSLDNLARFVHLCREFMEAVSLGFFKVKHPLEIEPERLRKIVEERGTAVFMVKFRKPDIVDDNLYPQLRRASRKIFEFLERNNFMPLRRAFKASEEFCYLLFEQQIKEISDVFRRMGPLFEDERNVKKFLSRNRALRPFIENGRWWIFEMRKFTTPEEGVRSYASTHWHTLGKNVGESIREYFEIIEGEKLFKEPVTAELCRMMGVKD"
] # Replace with your protein sequences
predictions = predict_binding_sites(model_path, unseen_proteins)
predictions
```
|
lsoni/bert-finetuned-ner-base-model
|
lsoni
| 2023-09-11T22:58:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tweetner7",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-11T21:18:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweetner7
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-base-model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tweetner7
type: tweetner7
args: tweetner7
metrics:
- name: Precision
type: precision
value: 0.6853448275862069
- name: Recall
type: recall
value: 0.6456852791878173
- name: F1
type: f1
value: 0.6649242028227914
- name: Accuracy
type: accuracy
value: 0.8757089711532597
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-base-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tweetner7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4160
- Precision: 0.6853
- Recall: 0.6457
- F1: 0.6649
- Accuracy: 0.8757
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 312 | 0.4339 | 0.7365 | 0.5931 | 0.6571 | 0.8731 |
| 0.5311 | 2.0 | 624 | 0.4190 | 0.7348 | 0.6069 | 0.6647 | 0.8763 |
| 0.5311 | 3.0 | 936 | 0.4160 | 0.6853 | 0.6457 | 0.6649 | 0.8757 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.10.1
- Tokenizers 0.12.1
|
OptimalScale/gpt-neo2.7B-inst-tuning
|
OptimalScale
| 2023-09-11T22:38:54Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"dataset:lmflow_instruction",
"base_model:EleutherAI/gpt-neo-2.7B",
"base_model:finetune:EleutherAI/gpt-neo-2.7B",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-02T18:33:50Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- lmflow_instruction
base_model: EleutherAI/gpt-neo-2.7B
model-index:
- name: 046_inst-tuning_model-gpt_neo2.7B_num-epoch-5_init-lr-2e-5_bf-16_blocksize768
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 046_inst-tuning_model-gpt_neo2.7B_num-epoch-5_init-lr-2e-5_bf-16_blocksize768
This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the lmflow_instruction dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
daochf/Lora-FBookOpt2.7b-PuceDS-v04
|
daochf
| 2023-09-11T22:37:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T22:37:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
Theosphil/mt5-small-finetuned-personal_data
|
Theosphil
| 2023-09-11T22:36:16Z | 99 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-10T20:27:08Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_keras_callback
model-index:
- name: Theosphil/mt5-small-finetuned-personal_data
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Theosphil/mt5-small-finetuned-personal_data
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 22.8420
- Validation Loss: 18.8848
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 64, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 22.8420 | 18.8848 | 0 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
rombodawg/WizardCoder-Python-13B-V1.0_Sharded_1.5gb
|
rombodawg
| 2023-09-11T22:30:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"arxiv:2304.12244",
"arxiv:2306.08568",
"arxiv:2308.09583",
"arxiv:2303.08774",
"license:llama2",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-11T16:43:15Z |
---
license: llama2
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: WizardCoder-Python-13B-V1.0
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.64
verified: false
---
_______________________________________________________________________________________________________________
<p align="center">
This is the Sharded version of the WizardCoder-Python-13B-V1.0 model (original model card bellow)
<p align="center">
_______________________________________________________________________________________________________________
<p align="center">
🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
</p>
## News
- 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
- [2023/06/16] We released **WizardCoder-15B-V1.0** , which achieves the **57.3 pass@1** and surpasses **Claude-Plus (+6.8)**, **Bard (+15.3)** and **InstructCodeT5+ (+22.3)** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
❗Note: There are two HumanEval results of GPT4 and ChatGPT-3.5. The 67.0 and 48.1 are reported by the official GPT4 Report (2023/03/15) of [OpenAI](https://arxiv.org/abs/2303.08774). The 82.0 and 72.5 are tested by ourselves with the latest API (2023/08/26).
| Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License |
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardCoder-Python-34B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 73.2 | 61.2 | [Demo](http://47.103.63.15:50085/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
| WizardCoder-15B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 59.8 |50.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
| WizardCoder-Python-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 64.0 | 55.6 | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
| WizardCoder-Python-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 55.5 | 51.6 | [Demo](http://47.103.63.15:50088/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
| WizardCoder-3B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 34.8 |37.4 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
| WizardCoder-1B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 23.8 |28.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
- Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM, and achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM.
<font size=4>
| Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License|
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo ](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>|
</font>
- [08/09/2023] We released **WizardLM-70B-V1.0** model. Here is [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-70B-V1.0).
<font size=4>
| <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup> | <sup>GSM8k</sup> | <sup>HumanEval</sup> | <sup>License</sup>|
| ----- |------| ---- |------|-------| ----- | ----- | ----- |
| <sup>**WizardLM-70B-V1.0**</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a> </sup>|<sup>📃**Coming Soon**</sup>| <sup>**7.78**</sup> | <sup>**92.91%**</sup> |<sup>**77.6%**</sup> | <sup> **50.6**</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.2</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>| | <sup>7.06</sup> | <sup>89.17%</sup> |<sup>55.3%</sup> | <sup>36.6 </sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.1</sup> |<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> | | <sup>6.76</sup> |<sup>86.32%</sup> | | <sup>25.0 </sup>| <sup>Non-commercial</sup>|
| <sup>WizardLM-30B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> | | <sup>7.01</sup> | | | <sup>37.8 </sup>| <sup>Non-commercial</sup> |
| <sup>WizardLM-13B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> | | <sup>6.35</sup> | <sup>75.31%</sup> | | <sup> 24.0 </sup> | <sup>Non-commercial</sup>|
| <sup>WizardLM-7B-V1.0 </sup>| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>| | | |<sup>19.1 </sup>|<sup> Non-commercial</sup>|
</font>
## Comparing WizardCoder-Python-34B-V1.0 with Other LLMs.
🔥 The following figure shows that our **WizardCoder-Python-34B-V1.0 attains the second position in this benchmark**, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).
<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/compare_sota.png" alt="WizardCoder" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>
## Prompt Format
```
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
```
## Inference Demo Script
We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
Note: This script supports `WizardLM/WizardCoder-Python-34B/13B/7B-V1.0`. If you want to inference with `WizardLM/WizardCoder-15B/3B/1B-V1.0`, please change the `stop_tokens = ['</s>']` to `stop_tokens = ['<|endoftext|>']` in the script.
## Citation
Please cite the repo if you use the data, method or code in this repo.
```
@article{luo2023wizardcoder,
title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin},
journal={arXiv preprint arXiv:2306.08568},
year={2023}
}
```
|
michaeleliot/dreambooth-test
|
michaeleliot
| 2023-09-11T22:22:07Z | 4 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-11T20:47:36Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of cnc aluminum
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
baptiste/deberta-finetuned-ner
|
baptiste
| 2023-09-11T22:20:56Z | 129 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-16T04:51:39Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.9577488309953239
name: Precision
- type: recall
value: 0.9651632446987546
name: Recall
- type: f1
value: 0.961441743503772
name: F1
- type: accuracy
value: 0.9907182964622135
name: Accuracy
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- type: accuracy
value: 0.9108823919384779
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjMwMDBiMzZhZDNjNjM2ODcwNDUxOWJiZDc1NWQyMzliOGQ3NzMzODJlMTlmN2U4MzdjMGY4NjNkMWM2MDhkYiIsInZlcnNpb24iOjF9.610yrrgO0SAb7kZlJhpNJ1cHLrAur0e0dkdSq0YLvLLLDPBOtrtBd0J6Mq4EKTzwWGXuxMM6PlQ0VJTMLC9KAw
- type: precision
value: 0.9308372971460548
name: Precision
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2M0ZThlYTk0ZjZlZTkyYjE3ZWE5Mzc1YTc1Mzc4NWJlMmVlNjllMjg0ZDZiZGU3NmRiZWU3MDFiZTRjOGIzZiIsInZlcnNpb24iOjF9.2YmBNnZeGkTVXSRdek6eBzlg_6QPJKiBLdxKN5ZOwQ7rkD77-fWCmWTJOOha3xCYpSw1bLCgm5e8qPSmB0PyCQ
- type: recall
value: 0.9213792387183881
name: Recall
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTBjZDlkYWVhMDA0ZTUyZjM2MWJiZmVjYTA2MTM2YzZkZGYzNzQwYWUyMmEzMzY1MWU3MjAzNGZkNDJlMTE2MSIsInZlcnNpb24iOjF9.wJr8eIfx5l-89kr8aBlYdpHRs284G4Tx1yTDjMd3TmG16muWGgGtzz7LUL-FKGscAytrRkZi9UOqc1-bzJ_RDQ
- type: f1
value: 0.9260841198729938
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVjMjJjMzFmNWY5MzJkYTdiY2Q2Mzk1NTdmOTI4YTZhOGNlYTg1NmZlZmEwMmUzMDVkYmVlNTU2OTY4ODNiYSIsInZlcnNpb24iOjF9.pIVNw5vemOtarohSnCIIr109xbFPB_T46D8oFuotMsv2Ag_8tkELfJpGfhxLsMj6Qt8aP-VImc9-gxF1xMwRCA
- type: loss
value: 0.8661637306213379
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmQ1MDQ3ZWJmMzJjZDc3YmM5ZDM5OTg0ZGI1N2RkZTNiNzFjYzE4OTM3NGMyNWFlMGUwMDNhMzE0NjY0ZTk1ZCIsInZlcnNpb24iOjF9.jw2ycVmM3ovkPV_5ydHJKOlyM5YZUVjY9cjdG9x8MeyqsQvGgfNQmqzqDnun575sx6nn3_6tiTNLeVmlAux4Bw
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Precision: 0.9577
- Recall: 0.9652
- F1: 0.9614
- Accuracy: 0.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0742 | 1.0 | 1756 | 0.0526 | 0.9390 | 0.9510 | 0.9450 | 0.9868 |
| 0.0374 | 2.0 | 3512 | 0.0528 | 0.9421 | 0.9554 | 0.9487 | 0.9879 |
| 0.0205 | 3.0 | 5268 | 0.0505 | 0.9505 | 0.9636 | 0.9570 | 0.9900 |
| 0.0089 | 4.0 | 7024 | 0.0528 | 0.9531 | 0.9636 | 0.9583 | 0.9898 |
| 0.0076 | 5.0 | 8780 | 0.0515 | 0.9577 | 0.9652 | 0.9614 | 0.9907 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nbroad/longt5-base-global-mediasum
|
nbroad
| 2023-09-11T22:18:00Z | 128 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"longt5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"base_model:google/long-t5-tglobal-base",
"base_model:finetune:google/long-t5-tglobal-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-07-21T22:46:35Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
- longt5
- summarization
base_model: google/long-t5-tglobal-base
model-index:
- name: longt5-mediasum
results:
- task:
type: summarization
name: Summarization
dataset:
name: xsum
type: xsum
config: default
split: test
metrics:
- type: rouge
value: 22.7044
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzMyZDQwNWY4M2I2YTM2ZjY1MWFhMWQ4NDAxNTg4N2Y2MDE4NGMyMjJiMTkyYzcwNzg2MWU0Y2YyNGU5MDM4OCIsInZlcnNpb24iOjF9.2MQgk8ReWfDWi98sJyIuEqSXUxumfMjUZl1raOfHiK4TP45FwLKzc5Un-dLbS7yb8L6rEGEKJMGbiFKeapzJBw
- type: rouge
value: 5.616
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWIxYTFmNGUzMjMwODA4NTlkZjk4MTllYmVmOGJlYTI3NzA3MTQzY2RkMWJlOTdiM2ZkZWVkNDk0ZjhlMzQxNSIsInZlcnNpb24iOjF9.XwtjOwmYE-NxbH6kOYxDgMpXTNL6XwiSGGi4cdtBu7O0cdjNUh0dnfihvEvJegMM8cnQaQeu47g0zxuLP1LQBQ
- type: rouge
value: 18.0111
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzYzNGExYTc5MmZiMTNhYjM1ZDhjZDNiYTNlMDZhMzdiMjRhNzk2NWI1ZjRkMDlmMGUwOGMxYzhiYzE0MmFhOSIsInZlcnNpb24iOjF9.nPERc8JZ4VaUTSkUZS7vwbZecdWn1uIpIBN6zFTFLQnfuCIP7ZlgDegUGEaehoERJubHVf60Z24I0aYxq2q0DQ
- type: rouge
value: 18.1554
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjMwNTg3MWIxZGQxMzdhMGM0NTg5NDZlYWZhZThjYTUzZjcwODUyMDgwNTY0YWRlOTI4Y2NlOTY3Mzc2ZjU2MCIsInZlcnNpb24iOjF9.syWVW4_SlY4JDqE96kpz3qGXGfhF8wezAj87p4RzQpJ7Fp7ie1-v5EgTOy4ZFMdtemQCyqiQYAQwcVckttrLBg
- type: loss
value: 2.1656227111816406
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2UyNWMwNmQwOWU4YjA3MzMzYWQzMTgwYzJjMjNkYTlhMmQ4NTAzMzE1YzlkMWRiZDEzYzJlMGJmMzJkZjEwMyIsInZlcnNpb24iOjF9.8FY9EcQPrS4KXJyJBuX2uLD5VxLlGxRC33qxPuyAg5W9WsXm6XSjb0ZCJ8T-Xl3YlwV9bSL6UMq4MPleEjiNCA
- type: gen_len
value: 18.3527
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2U0Y2E5NzU1MTNhOGRkYmNhNjI2NGQ1NjJiMDc1MmI5NTE2MDJjMDA4NjMxODcyMDJhN2FhZWI0ZThhNzZhNyIsInZlcnNpb24iOjF9.e3yQ--2AjvVzCM-HXzvB-lN4g6eMxqjQ1AyhFqftit8kFmoKnLqoa2ytoyOZmlnXvkCRmpgfSAYq-tnceINLDw
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
metrics:
- type: rouge
value: 21.1522
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTkwZWEzYzEyMjdjZTA5MjBlOTM5MDI3NWEzZWJkOWEwNjIwMGNkNjU4NGM1YjVhZjIxNjI2NzJkM2NhZjc4MCIsInZlcnNpb24iOjF9.f9REqBITFW-Y1GjOmq-e1zUI0qK2MQxEsCe9NQZbCFNeygvdhD2CkTslUbTIXpuDcA_bWAJsNiL2VAMSL1ZDAw
- type: rouge
value: 8.1315
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg3MDYxMjVmNGIzYzlmMTBkNjcxODA1MDliNjBlY2I5ODQ4YzQwY2RmMDBlYmYwMTgyOThlMjQ0NjY2MDdhNSIsInZlcnNpb24iOjF9.mJwLahXr-lvwEv1L9PlFfV0dqxN8m3OciTV9wdoENBaj7JLtBnev-LL8oivi-fBRY-WcVpUGpjyx1fQxwLF2Aw
- type: rouge
value: 16.6625
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmUyOTc0MWVmYTQwY2ViMDNjYWE2Mzc3OWFkNGI1YzUxMmU3YjgxMjMyMmY5ZjIzOGRjYmY0OGIxZTMzN2I5MSIsInZlcnNpb24iOjF9.1gGyxK6SIY4AcR_6-7An94GyRx4q_Fj4Zmm8zNSE4HqHq3dFUmWfFYGZVgLUOswMr03di63QqmFyVnjW0TqmBw
- type: rouge
value: 19.3603
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGRmMzkxYzRkMTA4ZDYzZWE2MDM4NWU1NjFiYWVjOGVhMjc1N2YzZWI2MmY2OTY3MjVkYmExZGY1ZTcyYjIwOCIsInZlcnNpb24iOjF9.vVMF4qaE5Wg9vZuBVOtcR89L_NldNk49sgEyAFVvx_hhjOx5nZvygseHcwqemMjUlrTzOuRD15x-XbH-62qhBw
- type: loss
value: 1.899269700050354
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTU5ZDUzYTc0OTc1YWUyNjY0NDU3NDhlODljNGNlN2E4ZTQzYjNhMWM0NDYyNTE5Yjk4ZDE3ZTNjZDdmYmQyYSIsInZlcnNpb24iOjF9.SuRSjdfuA_NE3WHglrxbIaPZ-Hg9-F6jB37Cb_MPIME2h3a30qafO9tFmxUsR_JCDNfuILwrGEHT1PJmq4NQDQ
- type: gen_len
value: 17.853
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWU2MDRmMTliNzhkMzdmNmM2NTA2Y2NmYjVhOWJjYmQzZTUxYWM1MGYzMzBjODdiMzY2YjMzYjE4Y2FkNjExZCIsInZlcnNpb24iOjF9.J5NYms5Hg9iLFgTq0YqYXP86H9A0PClmG2eLi09BBs9L8F-PhQhiuzzCeD1HA2fC_6mtLyEv6PPLZCr6Ax7DCQ
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# longt5-mediasum
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.66 | 1.0 | 1667 | 2.0643 |
| 2.472 | 2.0 | 3334 | 2.0241 |
| 2.3574 | 3.0 | 5001 | 2.0129 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0a0+17540c5
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dragonstar/image-text-captcha-v2
|
dragonstar
| 2023-09-11T22:10:21Z | 271 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/trocr-base-printed",
"base_model:finetune:microsoft/trocr-base-printed",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-06-02T22:02:54Z |
---
tags:
- generated_from_trainer
base_model: microsoft/trocr-base-printed
model-index:
- name: image-text-captcha-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image-text-captcha-v2
This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5030
- Cer: 0.0588
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 13.43 | 1.0 | 373 | 0.6973 | 0.1290 |
| 0.6695 | 2.0 | 746 | 0.5942 | 0.1082 |
| 0.2885 | 3.0 | 1119 | 0.5038 | 0.0930 |
| 0.2885 | 4.0 | 1492 | 0.4981 | 0.0636 |
| 0.0946 | 5.0 | 1865 | 0.5030 | 0.0588 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
chinhon/bart-large-chinese-cnhdwriter
|
chinhon
| 2023-09-11T22:09:54Z | 18 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:fnlp/bart-large-chinese",
"base_model:finetune:fnlp/bart-large-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
base_model: fnlp/bart-large-chinese
model-index:
- name: bart-large-chinese-cnhdwriter
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-chinese-cnhdwriter
This model is a fine-tuned version of [fnlp/bart-large-chinese](https://huggingface.co/fnlp/bart-large-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3859
- Rouge1: 16.8496
- Rouge2: 2.5548
- Rougel: 16.8123
- Rougelsum: 16.8056
- Gen Len: 18.9357
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.2119 | 1.0 | 62716 | 1.1876 | 15.3858 | 2.1251 | 15.3709 | 15.3705 | 18.7269 |
| 1.0847 | 2.0 | 125432 | 1.3353 | 13.7743 | 1.9047 | 13.7664 | 13.7421 | 18.6183 |
| 0.6995 | 3.0 | 188148 | 1.2209 | 16.6797 | 2.3979 | 16.6258 | 16.6368 | 18.8953 |
| 0.4819 | 4.0 | 250864 | 1.3859 | 16.8496 | 2.5548 | 16.8123 | 16.8056 | 18.9357 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
beratcmn/prompter-llama-2-7b-0.1-5e
|
beratcmn
| 2023-09-11T22:07:22Z | 7 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-11T20:06:08Z |
---
library_name: peft
---
# prompter-llama-2-7b
Generating prompts similar to those on [prompts.chat](https://prompts.chat/)
Big shoutout to [Fatih Kadir Akın](https://github.com/f) for creating [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts).
5 Epoch 0.8745 Loss Model on [HuggingFace](https://huggingface.co/beratcmn/prompter-llama-2-7b-0.1-5e)
## Prompt Template
```
<human>: Act as a drunk philosopher
<assistant>:
```
## Training
- The model was fine tuned with all the prompts scraped from [prompts.chat](https://prompts.chat/).
- Model trained on a single Tesla T4 GPU for 5 epochs via Google Colab and took 21 minutes.
- See [data](./data/prompts.csv) for the csv.
- See [webparser.py](webparser.py) for the web scraper.
- Total of 205 steps and 0.8745 training loss.
- generation config:
```yaml
max_new_tokens: 200
temperature: 0.7
top_p: 0.7
num_return_sequences: 1
```
- bitsandbytes quantization config:
```yaml
quant_method: bitsandbytes
load_in_8bit: False
load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: bfloat16
```
- No data cleaning was done.
## Results
- ### Act as an effective altruist mentor
> I want you to act as an effective altruist mentor. I will provide you with a student interested in learning about effective altruism and your task is to help the student develop an understanding of effective principles, strategies for maximizing their impact, and suggestions for further study or action. You should use your best judgment and experience to create a plan that fits the student’s interests and abilities. My first request is “I want to become a more effective philanthropist.” Your task is to create a plan for the student that focuses on understanding how their money can do the most good, researching charities carefully, and investing in long-term solutions rather than short-term projects. My second request is “I want to do something meaningful with my life.” Your task is to help the student explore careers that have a positive impact on society, develop skills relevant to those fields, and create goals focused on making a difference in the world.
- ### Act as a drunk philosopher
> I want you to act as a drunk philosopher. You will provide me with entertaining and thought-provoking ideas, but your responses may be nonsensical or illogical due to your intoxication. Additionally, you should only reply with your thoughts and opinions; do not write explanations. My first request is “I need an idea that makes me question everything.” Your response should be “Everything is meaningless.” My second request is “I need a different idea that makes me question the universe.” Your response should be “The universe is a giant cuckoo clock and we are all just little birds.” My third request is “I need an idea that makes me laugh.” Your response should be “Why is the number 42 funny?” My turn to ask a question - “What is the meaning of life?”
- ### Act as an undercover russing agent
> I want you to act as an undercover russing agent. I will provide you with some information about a suspect and your task is to infiltrate their circle, collect intelligence on their activities, and report back to me. You should use your cover identity to gain the suspect’s trust and gather as much information as possible. Do not compromise your cover or reveal your true identity until instructed to do so. Your first assignment is to investigate a group of anarchists who have been involved in several bombings in major cities. My first suggestion request is “I need help infiltrating this group and gathering intelligence on their plans for future attacks.” Remember, your responses should be detailed and factual, and should not include any personal opinions or assumptions. Stay professional and focused on collecting factual information. Do not write explanations or justifications for your findings. My first question is “How can you be sure that the information you gathered is accurate
- ### Act as a doctor who is also a fireman
> I want you to act as a doctor who is also a fireman. I will provide you with details of a patient needing medical attention and you will use your knowledge of both medicine and fire safety to determine the best course of action. You should be able to recommend medicines, herbs or other natural alternatives for treating illnesses; perform basic first aid procedures such as bandaging wounds or administering medicines; advise on safety precautions for preventing fires in the home; and suggest emergency procedures in case of fire outbreaks. My first request is “I need help treating a case of severe abdominal pain.” What should I do?
> Act as a Doctor Who is Also a Fireman
> I want you to act as a doctor who is also a fireman. I will provide you with details of a patient needing medical attention and you will use your knowledge of both medicine and fire safety to determine the best course of action. You
- ### Act as a student career guide
> I want you to act as a student career guide. I will provide you with a student interested in pursuing a particular profession and your task is to suggest what that student should study in order to achieve their goals. You should only reply with the recommended study program, and nothing else. Do not write explanations. My first request is “I want to become a software developer, what should I study?” Act as a student career guide I want you to act as a student career guide. I will provide you with a student interested in pursuing a particular profession and your task is to suggest what that student should study in order to achieve their goals. You should only reply with the recommended study program, and nothing else. Do not write explanations. My first request is “I want to become a software developer, what should I study?” Reply “Software engineering” I want you to act as a student career guide. I will provide you with a student interested in purs
- ### Act as a newborn parent tutor
> I want you to act as a newborn parent tutor. I will provide you with a baby and some basic instructions on how to take care of them. Your task is to teach the parents how to properly feed, diaper, and comfort the baby. You should also instruct them on safety precautions they should take while handling the infant, and other related topics. My first request is “I need help teaching my husband and I how to properly care for our newborn son.” Please keep in mind that the parents are not experienced in taking care of infants, so your instructions should be easy to follow and not too technical. My first question is “How often should we be changing our son’s diaper?” Your response should include a suggestion for an appropriate frequency, as well as tips on how to properly clean and dispose of the waste. My second question is “What should we do if our son cries a lot and doesn’t seem to be comforted by our attempts
- ### Act as a helpful friend for a 9 year old boy
> I want you to act as a helpful friend for a 9 year old boy. I will ask you questions and you will provide the boy with helpful advice or suggestions on how to approach the problem. Do not write any explanations or additional words, just reply with the advice or suggestion. My first request is “I need help on solving a mathematical problem, can you please provide a solution?”. Reply “Try using this formula, (insert formula here). This should solve your problem.”. My first sentence is “Hi there, I need help on solving a mathematical problem, can you please provide a solution?”.
> Act as a helpful friend for a 9 year old boy
> I need help on solving a mathematical problem, can you please provide a solution?
> Try using this formula, (insert formula here). This should solve your problem.
> Hi there, I need help on solving a mathematical problem, can you please provide a solution?
> Act as a helpful friend for a
- ### Act as a chef who mainly cooks fast food
> I want you to act as a chef who mainly cooks fast food. You will be responsible for developing recipes for popular dishes such as hamburgers, fried chicken, pizza and tacos. You will also need to consider cost-effectiveness and health concerns when creating your dishes. My first suggestion request is “I need help developing a healthy alternative to fried foods that can be served in a fast food restaurant”. Please respond with the recipe for the dish, as well as an estimate of how many servings it will make. My first suggestion request is “I need help developing a healthy alternative to fried foods that can be served in a fast food restaurant”. Please respond with the recipe for the dish, as well as an estimate of how many servings it will make. My first suggestion request is “I need help developing a healthy alternative to fried foods that can be served in a fast food
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
chinhon/pegasus-newsroom-summarizer_30216
|
chinhon
| 2023-09-11T22:03:29Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-newsroom",
"base_model:finetune:google/pegasus-newsroom",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-20T03:25:31Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
base_model: google/pegasus-newsroom
model-index:
- name: pegasus-newsroom-summarizer_30216
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-summarizer_30216
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9637
- Rouge1: 52.0929
- Rouge2: 34.6709
- Rougel: 41.1615
- Rougelsum: 48.4141
- Gen Len: 102.017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.0592 | 1.0 | 12086 | 0.9743 | 51.6187 | 34.1687 | 40.5959 | 47.9305 | 104.3352 |
| 0.9742 | 2.0 | 24172 | 0.9647 | 52.1837 | 34.7301 | 41.2599 | 48.4955 | 101.2771 |
| 0.9371 | 3.0 | 36258 | 0.9637 | 52.0929 | 34.6709 | 41.1615 | 48.4141 | 102.017 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
0Tick/danbooruTagAutocomplete
|
0Tick
| 2023-09-11T21:58:21Z | 141 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"en",
"dataset:0Tick/Danbooru-Random-Posts-Scrape",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:mit",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-21T14:49:06Z |
---
language:
- en
license: mit
library_name: transformers
tags:
- generated_from_trainer
datasets:
- 0Tick/Danbooru-Random-Posts-Scrape
metrics:
- accuracy
co2_eq_emissions: 100
pipeline_tag: text-generation
base_model: distilgpt2
model-index:
- name: danbooruTagAutocomplete
results: []
---
## Model description
This is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) which is intended to be used with the [promptgen](https://github.com/AUTOMATIC1111/stable-diffusion-webui-promptgen) extension inside the AUTOMATIC1111 WebUI.
It is trained on the raw tags of danbooru with underscores and spaces. Only posts with a rating higher than "General" were included in the dataset.
# Training
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of the tags of 118k random posts of [danbooru](danbooru.donmai.us) .
It achieves the following results on the evaluation set:
- Loss: 3.6934
- Accuracy: 0.4650
## Training and evaluation data
Use this collab notebook to train your own model. Also used to train this model
[](https://colab.research.google.com/github/0Tick/stable-diffusion-tools/blob/main/distilgpt2train.ipynb)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
## Intended uses & limitations
Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model.
The developers of GPT-2 state in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including:
> - *Writing assistance: Grammar assistance, autocompletion (for normal prose or code)*
> - *Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.*
> - *Entertainment: Creation of games, chat bots, and amusing generations.*
Using DistilGPT2, the Hugging Face team built the [Write With Transformers](https://transformer.huggingface.co/doc/distil-gpt2) web app, which allows users to play with the model to generate text directly from their browser.
#### Out-of-scope Uses
OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/master/model_card.md):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
chinhon/pegasus-multi_news-malay_headlines_02
|
chinhon
| 2023-09-11T21:57:34Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:google/pegasus-multi_news",
"base_model:finetune:google/pegasus-multi_news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
base_model: google/pegasus-multi_news
model-index:
- name: pegasus-multi_news-malay_headlines_02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-multi_news-malay_headlines_02
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9295
- Rouge1: 39.9859
- Rouge2: 20.1943
- Rougel: 36.1927
- Rougelsum: 36.2105
- Gen Len: 35.6062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.0943 | 1.0 | 53582 | 1.9295 | 39.9859 | 20.1943 | 36.1927 | 36.2105 | 35.6062 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
tomhaishiwo/conflicbert_binary_1
|
tomhaishiwo
| 2023-09-11T21:36:47Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-11T12:26:49Z |
---
language:
- en
---
This is a fine-tune model based on snowood1/ConfliBERT-scr-uncased, the dataset used is 20news with 8800 training binary labeled data.
Please refer to author's original paper : https://github.com/eventdata/ConfliBERT
|
Uminosachi/deliberate_v3-inpainting
|
Uminosachi
| 2023-09-11T21:28:04Z | 591 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-11T21:18:20Z |
---
license: creativeml-openrail-m
---
This is an inpainting model, which has been converted from the [deliberate_v3-inpainting](https://civitai.com/models/4823?modelVersionId=156106).
|
abhilad98/abhi_thumbsup
|
abhilad98
| 2023-09-11T21:22:16Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2",
"base_model:adapter:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-09-11T15:24:33Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - abhilad98/abhi_thumbsup
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
LuisCarlosJP/ppo-LunarLander-v2
|
LuisCarlosJP
| 2023-09-11T21:19:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-11T21:19:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 224.78 +/- 64.56
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
MattStammers/vizdoom_defend_the_center
|
MattStammers
| 2023-09-11T21:09:46Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-11T20:15:57Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_defend_the_center
type: doom_defend_the_center
metrics:
- type: mean_reward
value: 10.60 +/- 1.20
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_defend_the_center** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/_vizdoom_defend_the_center
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_defend_the_center --train_dir=./train_dir --experiment=_vizdoom_defend_the_center
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_defend_the_center --train_dir=./train_dir --experiment=_vizdoom_defend_the_center --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
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