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Model Details import evaluate

eval_results = task_evaluator.compute( model_or_pipeline="lvwerra/distilbert-imdb", data=data, metric=evaluate.combine(["accuracy", "recall", "precision", "f1"]), label_mapping={"NEGATIVE": 0, "POSITIVE": 1} ) print(eval_results)

Model Description {

'accuracy': 0.918,
'f1': 0.916,
'precision': 0.9147,
'recall': 0.9187,
'latency_in_seconds': 0.013,
'samples_per_second': 78.887,
'total_time_in_seconds': 12.676

}

git clone [email protected]:huggingface/lm-evaluation-harness.git

cd lm-evaluation-harness git checkout main pip install -e . lm-eval --model_args="pretrained=,revision=,dtype=" --tasks=leaderboard --batch_size=auto --output_path=

Developed by: [More Information Needed] curl https://router.huggingface.co/novita/v3/openai/chat/completions
-H "Authorization: Bearer $HF_TOKEN"
-H 'Content-Type: application/json'
-d '{ "messages": [ { "role": "user", "content": "How many G in huggingface?" } ], "model": "deepseek/deepseek-v3-0324", "stream": false }'
Funded by [optional]: [More Information Needed] python -c "import evaluate; print(evaluate.load('exact_match').compute(references=['hello'], predictions=['hello']))"
Shared by [optional]: [More Information Needed] {'exact_match': 1.0}
Model type: [More Information Needed] git clone https://github.com/huggingface/evaluate.git cd evaluate pip install -e .
Language(s) (NLP): [More Information Needed] precision_metric = evaluate.load("precision") results = precision_metric.compute(references=[0, 1], predictions=[0, 1]) print(results)
License: [More Information Needed] from evaluate import load squad_metric = load("squad") predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}] references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}] results = squad_metric.compute(predictions=predictions, references=references) results
Finetuned from model [optional]: [More Information Needed] from datasets import load_dataset from evaluate import evaluator from transformers import AutoModelForSequenceClassification, pipeline

data = load_dataset("imdb", split="test").shuffle(seed=42).select(range(1000)) task_evaluator = evaluator("text-classification")

1. Pass a model name or path

eval_results = task_evaluator.compute( model_or_pipeline="lvwerra/distilbert-imdb", data=data, label_mapping={"NEGATIVE": 0, "POSITIVE": 1} )

2. Pass an instantiated model

model = AutoModelForSequenceClassification.from_pretrained("lvwerra/distilbert-imdb")

eval_results = task_evaluator.compute( model_or_pipeline=model, data=data, label_mapping={"NEGATIVE": 0, "POSITIVE": 1} )

3. Pass an instantiated pipeline

pipe = pipeline("text-classification", model="lvwerra/distilbert-imdb")

eval_results = task_evaluator.compute( model_or_pipeline=pipe, data=data, label_mapping={"NEGATIVE": 0, "POSITIVE": 1} ) print(eval_results)

Model Sources [optional]

mkdir ~/my-project

cd ~/my-project # Activate the virtual environment source .env/bin/activate

Deactivate the virtual environment

source .env/bin/deactivate

Repository: [More Information Needed] https://huggingface.co/docs/optimum/index
Paper [optional]: [More Information Needed] https://huggingface.co/docs/optimum/installation
Demo [optional]: [More Information Needed] python -m pip install git+https://github.com/huggingface/optimum.git

Uses

Direct Use

[More Information Needed] curl https://uu149rez6gw9ehej.eu-west-1.aws.endpoints.huggingface.cloud/distilbert-sentiment
-X POST
-d '{"inputs": "Deploying my first endpoint was an amazing experience."}'
-H "Authorization: Bearer "

Downstream Use [optional]

[More Information Needed] curl --request POST
--url https://uu149rez6gw9ehej.eu-west-1.aws.endpoints.huggingface.cloud/wav2vec-asr
--header 'Authorization: Bearer '
--header 'Content-Type: audio/x-flac'
--data-binary '@sample1.flac'

Out-of-Scope Use

[More Information Needed] const inference = new HfInference('hf_...') // your user token

Bias, Risks, and Limitations

[More Information Needed] const output = await inference.request({ inputs: "blablabla", parameters: { custom_parameter_1: ..., ... } });

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed] https://huggingface.co/docs/inference-endpoints/guides/advanced#advanced-setup-instance-types-auto-scaling-versioning

Training Details

Training Data

[More Information Needed] optimum[onnxruntime]==1.2.3 !cd distilbert-base-uncased-emotion && touch handler.py mkl-include mkl

Training Procedure

Preprocessing [optional]

[More Information Needed] # install git-lfs to interact with the repository sudo apt-get update sudo apt-get install git-lfs

install transformers (not needed since it is installed by default in the container)

pip install transformers[sklearn,sentencepiece,audio,vision] git lfs install git clone https://huggingface.co/philschmid/distilbert-base-uncased-emotion

setup cli with token

huggingface-cli login git config --global credential.helper store

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed] from typing import Dict, List, Any

class EndpointHandler(): def init(self, path=""): # Preload all the elements you are going to need at inference. # pseudo: # self.model= load_model(path)

def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
   data args:
        inputs (:obj: `str` | `PIL.Image` | `np.array`)
        kwargs
  Return:
        A :obj:`list` | `dict`: will be serialized and returned
    """

    # pseudo
    # self.model(input)

Evaluation

import pandas as pd

from datasets import load_dataset from evaluate import evaluator from transformers import pipeline

models = [ "xlm-roberta-large-finetuned-conll03-english", "dbmdz/bert-large-cased-finetuned-conll03-english", "elastic/distilbert-base-uncased-finetuned-conll03-english", "dbmdz/electra-large-discriminator-finetuned-conll03-english", "gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner", "philschmid/distilroberta-base-ner-conll2003", "Jorgeutd/albert-base-v2-finetuned-ner", ]

data = load_dataset("conll2003", split="validation").shuffle().select(1000) task_evaluator = evaluator("token-classification")

results = [] for model in models: results.append( task_evaluator.compute( model_or_pipeline=model, data=data, metric="seqeval" ) )

df = pd.DataFrame(results, index=models) df[["overall_f1", "overall_accuracy", "total_time_in_seconds", "samples_per_second", "latency_in_seconds"]]

Testing Data, Factors & Metrics

Testing Data

[More Information Needed] !echo "holidays" >> requirements.txt !pip install -r requirements.txt

Factors

[More Information Needed] from typing import Dict, List, Any from transformers import pipeline import holidays

class EndpointHandler(): def init(self, path=""): self.pipeline = pipeline("text-classification",model=path) self.holidays = holidays.US()

def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
   data args:
        inputs (:obj: `str`)
        date (:obj: `str`)
  Return:
        A :obj:`list` | `dict`: will be serialized and returned
    """
    # get inputs
    inputs = data.pop("inputs",data)
    date = data.pop("date", None)

    # check if date exists and if it is a holiday
    if date is not None and date in self.holidays:
      return [{"label": "happy", "score": 1}]


    # run normal prediction
    prediction = self.pipeline(inputs)
    return prediction

Metrics

[More Information Needed] # add all our new files !git add *

commit our files

!git commit -m "add custom handler"

push the files to the hub

!git push

Results

[More Information Needed] tensorflow_model_server
--rest_api_port=5000
--model_name=my_model
--model_base_path="/repository"

Summary

Model Examination [optional]

[More Information Needed] https://huggingface.co/docs/inference-endpoints/guides/logs

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed] { "inputs": "This is a sample input", "moreData": 1, "customTask": true }
Hours used: [More Information Needed] { https://huggingface.co/docs/transformers.js/index }
Cloud Provider: [More Information Needed] { "inputs": { "text": "This sound track was beautiful!", "text_pair": "It paints the scenery in your mind so well I would recomend it even to people who hate vid. game music!" } }{ "inputs": "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!", "parameters": { "candidate_labels": ["refund", "legal", "faq"] } }
Carbon Emitted: [More Information Needed]

}

Technical Specifications [optional] import datasets

from transformers import pipeline from transformers.pipelines.pt_utils import KeyDataset from tqdm.auto import tqdm

pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0) dataset = datasets.load_dataset("superb", name="asr", split="test")

KeyDataset (only pt) will simply return the item in the dict returned by the dataset item

as we're not interested in the target part of the dataset. For sentence pair use KeyPairDataset

for out in tqdm(pipe(KeyDataset(dataset, "file"))): print(out) # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"} # {"text": ....} # ....

Model Architecture and Objective

[More Information Needed] { "inputs": { "question": "What is used for inference?", "context": "My Name is Philipp and I live in Nuremberg. This model is used with sagemaker for inference." } }

Compute Infrastructure

[More Information Needed] { "inputs": "This sound track was

Hardware

[More Information Needed] { default_args = { "output_dir": "tmp", "eval_strategy": "steps", "num_train_epochs": 1, "log_level": "error", "report_to": "none", } }

Software

[More Information Needed] }Tue Jan 11 08:58:05 2022 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 460.91.03 Driver Version: 460.91.03 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla V100-SXM2... On | 00000000:00:04.0 Off | 0 | | N/A 37C P0 39W / 300W | 2631MiB / 16160MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | 0 N/A N/A 3721 C ...nvs/codeparrot/bin/python 2629MiB | +-----------------------------------------------------------------------------+

Citation [optional]

BibTeX:

[More Information Needed] @misc{romeo_rosete_2025, author = { Romeo Rosete }, title = { romeo-rosete (Revision d0f042d) }, year = 2025, url = { https://huggingface.co/roseteromeo56/romeo-rosete }, doi = { 10.57967/hf/5116 }, publisher = { Hugging Face } } {

APA:

[More Information Needed] {"inputs": [ { "role": "user", "content": "Which movie is the best ?"

Glossary [optional]

[More Information Needed] { "model": { "image": { "huggingface": { "env": { "var1": "value" } } }, }

More Information [optional]

Model Card Authors [optional]

Model Card Contact