BAAI/bge-large-en-v1.5 trained for News Type Classification

This is a Cross Encoder model finetuned from BAAI/bge-large-en-v1.5 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text pair classification.

Model Details

Model Description

Model Type: Cross Encoder
Base model: BAAI/bge-large-en-v1.5
Maximum Sequence Length: 512 tokens
Number of Output Labels: 10 labels
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ["Dennis Schroder potential trade piece, but Brooklyn Nets won't just give him away", 'Basketball: Pre Game - Entity: Dennis Schroder'],
    ['The Vancouver Canucks are currently facing a cap crunch after the Lafferty pickup (Sam) Lafferty.', 'Hockey: Pre Game - Entity: Lafferty'],
    ['Additional news for 12/11 Out: Moses Moody Questionable: Steven Adams Not on injury report: Tari Eason', 'Basketball: Pre Game - Entity: Moses Moody'],
    ['Additional news for 12/11 Out: Moses Moody Questionable: Steven Adams Not on injury report: Tari Eason', 'Basketball: Pre Game - Entity: Steven Adams'],
    ['Additional news for 12/11 Out: Moses Moody Questionable: Steven Adams Not on injury report: Tari Eason', 'Basketball: Pre Game - Entity: Tari Eason'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5, 10)

Training Details

Training Dataset

Unnamed Dataset

Size: 7,434 training samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label
type	string	string	list
details	min: 13 characters mean: 141.82 characters max: 320 characters	min: 31 characters mean: 39.51 characters max: 53 characters	size: 10 elements

Samples:

sentence1	sentence2	label
`Dennis Schroder potential trade piece, but Brooklyn Nets won't just give him away`	`Basketball: Pre Game - Entity: Dennis Schroder`	`[0.0, 0.0, 0.0, 0.0, 0.0, ...]`
`The Vancouver Canucks are currently facing a cap crunch after the Lafferty pickup (Sam) Lafferty.`	`Hockey: Pre Game - Entity: Lafferty`	`[0.0, 0.0, 0.0, 1.0, 0.0, ...]`
`Additional news for 12/11 Out: Moses Moody Questionable: Steven Adams Not on injury report: Tari Eason`	`Basketball: Pre Game - Entity: Moses Moody`	`[0.0, 1.0, 0.0, 0.0, 1.0, ...]`

Loss: main.MultiLabelCrossEntropyLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 10
warmup_ratio: 0.1
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
tp_size: 0
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0022	1	1.0293
0.4301	200	0.9024
0.8602	400	0.4968
1.2903	600	0.3217
1.7204	800	0.2909
2.1505	1000	0.2133
2.5806	1200	0.1821
3.0108	1400	0.1737
3.4409	1600	0.1152
3.8710	1800	0.1224
4.3011	2000	0.1019
4.7312	2200	0.0909
5.1613	2400	0.0806
5.5914	2600	0.0634
6.0215	2800	0.0689
6.4516	3000	0.0503
6.8817	3200	0.0532
7.3118	3400	0.0453
7.7419	3600	0.0405
8.1720	3800	0.032
8.6022	4000	0.029
9.0323	4200	0.032
9.4624	4400	0.0246
9.8925	4600	0.0245

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.18
Sentence Transformers: 5.1.1
Transformers: 4.51.0
PyTorch: 2.8.0
Accelerate: 1.10.1
Datasets: 4.1.1
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for corleymj/snippet-entity-news-classification-v2

Base model

BAAI/bge-large-en-v1.5

Finetuned

(82)

this model

Paper for corleymj/snippet-entity-news-classification-v2

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 13