Instructions to use djamina/relatives_psr_seq-cbert_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djamina/relatives_psr_seq-cbert_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="djamina/relatives_psr_seq-cbert_finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("djamina/relatives_psr_seq-cbert_finetuned") model = AutoModelForTokenClassification.from_pretrained("djamina/relatives_psr_seq-cbert_finetuned") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -10,6 +10,11 @@ metrics:
|
|
| 10 |
model-index:
|
| 11 |
- name: relatives_psr_seq-cbert_finetuned
|
| 12 |
results: []
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
---
|
| 14 |
|
| 15 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -66,4 +71,4 @@ The following hyperparameters were used during training:
|
|
| 66 |
- Transformers 4.41.2
|
| 67 |
- Pytorch 2.3.0+cu121
|
| 68 |
- Datasets 2.19.2
|
| 69 |
-
- Tokenizers 0.19.1
|
|
|
|
| 10 |
model-index:
|
| 11 |
- name: relatives_psr_seq-cbert_finetuned
|
| 12 |
results: []
|
| 13 |
+
datasets:
|
| 14 |
+
- djamina/relatives_psr
|
| 15 |
+
language:
|
| 16 |
+
- fr
|
| 17 |
+
pipeline_tag: token-classification
|
| 18 |
---
|
| 19 |
|
| 20 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 71 |
- Transformers 4.41.2
|
| 72 |
- Pytorch 2.3.0+cu121
|
| 73 |
- Datasets 2.19.2
|
| 74 |
+
- Tokenizers 0.19.1
|