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camembertv2-base-xnli

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metadata
language: fr
license: mit
tags:
  - roberta
  - text-classification
  - nli
base_model: almanach/camembertv2-base
datasets:
  - FLUE-XNLI
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: transformers
model-index:
  - name: almanach/camembertv2-base-xnli
    results:
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: flue-XNLI
          name: FLUE-XNLI
        metrics:
          - name: accuracy
            type: accuracy
            value: 0.82851
            verified: false

Model Card for almanach/camembertv2-base-xnli

almanach/camembertv2-base-xnli is a roberta model for text classification. It is trained on the FLUE-XNLI dataset for the task of Natural Language Inference. The model achieves an accuracy of 0.82851 on the FLUE-XNLI dataset.

The model is part of the almanach/camembertv2-base family of model finetunes.

Model Details

Model Description

  • Developed by: Wissam Antoun (Phd Student at Almanach, Inria-Paris)
  • Model type: roberta
  • Language(s) (NLP): French
  • License: MIT
  • Finetuned from model [optional]: almanach/camembertv2-base

Model Sources [optional]

Uses

The model can be used for text classification tasks in French for Natural Language Inference.

Bias, Risks, and Limitations

The model may exhibit biases based on the training data. The model may not generalize well to other datasets or tasks. The model may also have limitations in terms of the data it was trained on.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model = AutoModelForSequenceClassification.from_pretrained("almanach/camembertv2-base-xnli")
tokenizer = AutoTokenizer.from_pretrained("almanach/camembertv2-base-xnli")

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

classifier({
    "text": "Le livre est très intéressant et j'ai appris beaucoup de choses.",
    "text_pair": "Le livre est très ennuyeux et je n'ai rien appris.",
})

Training Details

Training Data

The model is trained on the FLUE-XNLI dataset.

  • Dataset Name: FLUE-XNLI
  • Dataset Size:
    • Train: 49399
    • Dev: 1988
    • Test: 2000

Training Procedure

Model trained with the run_xnli.py script from the huggingface repository.

Training Hyperparameters 

accelerator_config: '{''split_batches'': False, ''dispatch_batches'': None, ''even_batches'':
  True, ''use_seedable_sampler'': True, ''non_blocking'': False, ''gradient_accumulation_kwargs'':
  None}'
adafactor: false
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1.0e-08
auto_find_batch_size: false
base_model: camembertv2
base_model_name: camembertv2-base-bf16-p2-17000
batch_eval_metrics: false
bf16: false
bf16_full_eval: false
data_seed: 666.0
dataloader_drop_last: false
dataloader_num_workers: 0
dataloader_persistent_workers: false
dataloader_pin_memory: true
dataloader_prefetch_factor: .nan
ddp_backend: .nan
ddp_broadcast_buffers: .nan
ddp_bucket_cap_mb: .nan
ddp_find_unused_parameters: .nan
ddp_timeout: 1800
debug: '[]'
deepspeed: .nan
disable_tqdm: false
dispatch_batches: .nan
do_eval: true
do_predict: false
do_train: true
epoch: 10.0
eval_accumulation_steps: 4
eval_accuracy: 0.8285140562248996
eval_delay: 0
eval_do_concat_batches: true
eval_loss: 0.5347269773483276
eval_on_start: false
eval_runtime: 6.7497
eval_samples: 2490
eval_samples_per_second: 368.907
eval_steps: .nan
eval_steps_per_second: 46.224
eval_strategy: epoch
eval_use_gather_object: false
evaluation_strategy: epoch
fp16: false
fp16_backend: auto
fp16_full_eval: false
fp16_opt_level: O1
fsdp: '[]'
fsdp_config: '{''min_num_params'': 0, ''xla'': False, ''xla_fsdp_v2'': False, ''xla_fsdp_grad_ckpt'':
  False}'
fsdp_min_num_params: 0
fsdp_transformer_layer_cls_to_wrap: .nan
full_determinism: false
gradient_accumulation_steps: 4
gradient_checkpointing: false
gradient_checkpointing_kwargs: .nan
greater_is_better: true
group_by_length: false
half_precision_backend: auto
hub_always_push: false
hub_model_id: .nan
hub_private_repo: false
hub_strategy: every_save
hub_token: <HUB_TOKEN>
ignore_data_skip: false
include_inputs_for_metrics: false
include_num_input_tokens_seen: false
include_tokens_per_second: false
jit_mode_eval: false
label_names: .nan
label_smoothing_factor: 0.0
learning_rate: 1.0e-05
length_column_name: length
load_best_model_at_end: true
local_rank: 0
log_level: debug
log_level_replica: warning
log_on_each_node: true
logging_dir: /scratch/camembertv2/runs/results/xnli/camembertv2-base-bf16-p2-17000/max_seq_length-160-gradient_accumulation_steps-4-precision-fp32-learning_rate-1e-05-epochs-10-lr_scheduler-cosine-warmup_steps-0.1/SEED-666/logs
logging_first_step: false
logging_nan_inf_filter: true
logging_steps: 100
logging_strategy: steps
lr_scheduler_kwargs: '{}'
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_steps: -1
metric_for_best_model: accuracy
mp_parameters: .nan
name: camembertv2/runs/results/xnli/camembertv2-base-bf16-p2-17000/max_seq_length-160-gradient_accumulation_steps-4-precision-fp32-learning_rate-1e-05-epochs-10-lr_scheduler-cosine-warmup_steps-0.1
neftune_noise_alpha: .nan
no_cuda: false
num_train_epochs: 10.0
optim: adamw_torch
optim_args: .nan
optim_target_modules: .nan
output_dir: /scratch/camembertv2/runs/results/xnli/camembertv2-base-bf16-p2-17000/max_seq_length-160-gradient_accumulation_steps-4-precision-fp32-learning_rate-1e-05-epochs-10-lr_scheduler-cosine-warmup_steps-0.1/SEED-666
overwrite_output_dir: false
past_index: -1
per_device_eval_batch_size: 8
per_device_train_batch_size: 8
per_gpu_eval_batch_size: .nan
per_gpu_train_batch_size: .nan
prediction_loss_only: false
push_to_hub: false
push_to_hub_model_id: .nan
push_to_hub_organization: .nan
push_to_hub_token: <PUSH_TO_HUB_TOKEN>
ray_scope: last
remove_unused_columns: true
report_to: '[''tensorboard'']'
restore_callback_states_from_checkpoint: false
resume_from_checkpoint: .nan
run_name: /scratch/camembertv2/runs/results/xnli/camembertv2-base-bf16-p2-17000/max_seq_length-160-gradient_accumulation_steps-4-precision-fp32-learning_rate-1e-05-epochs-10-lr_scheduler-cosine-warmup_steps-0.1/SEED-666
save_on_each_node: false
save_only_model: false
save_safetensors: true
save_steps: 500
save_strategy: epoch
save_total_limit: .nan
seed: 666
skip_memory_metrics: true
split_batches: .nan
tf32: .nan
torch_compile: true
torch_compile_backend: inductor
torch_compile_mode: .nan
torch_empty_cache_steps: .nan
torchdynamo: .nan
total_flos: 1.617427903829713e+17
tpu_metrics_debug: false
tpu_num_cores: .nan
train_loss: 0.3309724763735177
train_runtime: 41426.0671
train_samples: 392702
train_samples_per_second: 94.796
train_steps_per_second: 2.962
use_cpu: false
use_ipex: false
use_legacy_prediction_loop: false
use_mps_device: false
warmup_ratio: 0.1
warmup_steps: 0
weight_decay: 0.0

Results

Accuracy: 0.82851

Technical Specifications

Model Architecture and Objective

roberta for sequence classification.

Citation

BibTeX:

@misc{antoun2024camembert20smarterfrench,
      title={CamemBERT 2.0: A Smarter French Language Model Aged to Perfection},
      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
      year={2024},
      eprint={2411.08868},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.08868},
}