Instructions to use Shaelois/MeetingScript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaelois/MeetingScript with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Shaelois/MeetingScript")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shaelois/MeetingScript") model = AutoModelForSeq2SeqLM.from_pretrained("Shaelois/MeetingScript") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -28,7 +28,7 @@ pipeline_tag: summarization
|
|
| 28 |
**MeetingScript** is a sequence‑to‑sequence model based on
|
| 29 |
[google/bigbird-pegasus-large-bigpatent](https://huggingface.co/google/bigbird-pegasus-large-bigpatent)
|
| 30 |
and fine‑tuned on the [MeetingBank](https://huggingface.co/datasets/huuuyeah/meetingbank) corpus of meeting transcripts paired with human‐written summaries.
|
| 31 |
-
It is designed to take long meeting transcripts (up to
|
| 32 |
|
| 33 |
---
|
| 34 |
|
|
@@ -46,6 +46,6 @@ Evaluated on the held‑out test split of MeetingBank (≈ 600 transcripts), us
|
|
| 46 |
---
|
| 47 |
## Training Data
|
| 48 |
Dataset: MeetingBank
|
| 49 |
-
Splits: Train (~
|
| 50 |
-
Preprocessing: Sliding‑window chunking for sequences >
|
| 51 |
|
|
|
|
| 28 |
**MeetingScript** is a sequence‑to‑sequence model based on
|
| 29 |
[google/bigbird-pegasus-large-bigpatent](https://huggingface.co/google/bigbird-pegasus-large-bigpatent)
|
| 30 |
and fine‑tuned on the [MeetingBank](https://huggingface.co/datasets/huuuyeah/meetingbank) corpus of meeting transcripts paired with human‐written summaries.
|
| 31 |
+
It is designed to take long meeting transcripts (up to 4096 tokens) and produce concise, coherent summaries.
|
| 32 |
|
| 33 |
---
|
| 34 |
|
|
|
|
| 46 |
---
|
| 47 |
## Training Data
|
| 48 |
Dataset: MeetingBank
|
| 49 |
+
Splits: Train (~5000), Validation (~600), Test (~600)
|
| 50 |
+
Preprocessing: Sliding‑window chunking for sequences > 4096 tokens
|
| 51 |
|